Luận văn thạc sĩ Công nghệ thực phẩm: Using near-infrared spectroscopy to authenticate the geographical indicator (GI) of coffee and evaluate the effectiveness of GI on purchase intention

VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY HO CHI MINH UNIVERSITY OF TECHNOLOGY

-✩ -TO PHAN CHIEU DAN

USING NEAR-INFRARED SPECTROSCOPY TO AUTHENTICATE THE GEOGRAPHICAL INDICATOR

(GI) OF COFFEE AND EVALUATE THE

EFFECTIVENESS OF GI ON PURCHASE INTENTION

Major: Food Technology ID: 8540101

MASTER THESIS

Ho Chi Minh city, July 2022

ĐẠI HỌC QUỐC GIA TP HCM TRƯỜNG ĐẠI HỌC BÁCH KHOA

-✩ -TÔ PHAN CHIÊU ĐAN

SỬ DỤNG QUANG PHỔ CẬN HỒNG NGOẠI ĐỂ XÁC THỰC NGUỒN GỐC ĐỊA LÝ (GI) CÀ PHÊ VÀ ĐÁNH GIÁ MỨC ĐỘ HIỆU QUẢ CỦA GI ĐẾN Ý ĐỊNH

MUA HÀNG CỦA NGƯỜI TIÊU DÙNG

Chuyên ngành: Công nghệ thực phẩm Mã số: 8540101

LUẬN VĂN THẠC SĨ

TP HỒ CHÍ MINH, tháng 07 năm 2022

CÔNG TRÌNH ĐƯỢC HOÀN THÀNH TẠI: TRƯỜNG ĐẠI HỌC BÁCH KHOA – ĐHQG-HCM

Cán bộ hướng dẫn khoa học 1: Tiến sĩ Nguyễn Quốc Cường

Cán bộ hướng dẫn khoa học 2: Phó Giáo sư Tiến sĩ Lê Nguyễn Đoan Duy………

Cán bộ chấm nhận xét 1: Tiến sĩ, Nguyễn Hoài Hương

Cán bộ chấm nhận xét 2: PGS Tiến sĩ Hoàng Kim Anh

Luận văn thạc sĩ được bảo vệ tại Trường Đại học Bách Khoa, ĐHQG Tp HCM ngày 13 tháng 07 năm 2022 Thành phần Hội đồng đánh giá luận văn thạc sĩ gồm: 1 Chủ tịch hội đồng: Phó Giáo Sư Tiến Sĩ Tôn Nữ Minh Nguyệt

2 Phản biện 1: Tiến Sĩ Nguyễn Hoài Hương ………

3 Phản biện 2: Phó Giáo Sư Tiến Sĩ Hoàng Kim Anh

4 Ủy viên: Tiến Sĩ Nguyễn Quốc Cường… ………

5 Thư kí: Phó Giáo Sư Tiến Sĩ Trần Thị Thu Trà……… Xác nhận của Chủ tịch Hội đồng đánh giá LV và Trưởng Khoa quản lý chuyên ngành sau khi luận văn đã được sửa chữa (nếu có)

CHỦ TỊCH HỘI ĐỒNG TRƯỞNG KHOA KỸ THUẬT HÓA HỌC

ĐẠI HỌC QUỐC GIA TP.HCM CỘNG HÒA XÃ HỘI CHỦ NGHĨA VIỆT NAM TRƯỜNG ĐẠI HỌC BÁCH KHOA Độc lập - Tự do - Hạnh phúc

NHIỆM VỤ LUẬN VĂN THẠC SĨ

Họ tên học viên: Tô Phan Chiêu Đan MSHV: 1970372………… Ngày, tháng, năm sinh: 03/09/1997 Nơi sinh: Quảng Ngãi…… Chuyên ngành: Công nghệ thực phẩm Mã số : 8540101

I.TÊN ĐỀ TÀI: Sử dụng quang phổ cận hồng ngoại để xác thực nguồn gốc địa lý

(GI) cà phê và đánh giá mức độ hiệu quả của GI đến ý định mua hàng của người tiêu dùng

NHIỆM VỤ VÀ NỘI DUNG:

1/ Nhiệm vụ 1: Sử dụng dữ liệu phổ quang phổ cận hồng ngoại để xác thực nguồn

gốc địa lý (GI) cà phê

Nội dung: Xây dựng mô hình phân biệt các loại cà phê bằng dữ liệu phổ cận hồng

ngoại thu bằng máy quang phổ cận hồng ngoại cầm tay kết hợp với xử lý số liệu đa biến SIMCA, PLS-DA

2/ Nhiệm vụ 2: Đánh giá mức độ ảnh hưởng của chứng nhận GI đến ý định mua

hàng của người tiêu dùng

Nội dung: Sử dụng đánh giá tập trung (Central Location Test) để đánh giá về chất

lượng bên trong và bên ngoài bao bì cà phê, kết hợp với phân tích Conjoint các yếu tố để đánh giá được yếu tố ảnh hưởng đến ý định mua của người tiêu dùng ở Hồ Chí Minh

III NGÀY HOÀN THÀNH NHIỆM VỤ: 22/05/2022

IV.CÁN BỘ HƯỚNG DẪN (Ghi rõ học hàm, học vị, họ, tên):

1/ Tiến sĩ Nguyễn Quốc Cường

2/ Phó Giáo sư, Tiến sĩ Lê Nguyễn Đoan Duy

Tp HCM, ngày 01 tháng 07 năm 2022

CÁN BỘ HƯỚNG DẪN CHỦ NHIỆM BỘ MÔN ĐÀO TẠO

ACKNOWLEDGMENT

First of all, we would like to send our sincere appreciation and deepest thanks to our instructors Dr Nguyen Quoc Cuong and Assoc Prof Dr Le Nguyen Doan Duy, for guiding us through the study of this thesis They have always been by our side, willing to sit down and discuss problems or support new knowledge which we have not had a chance to study before, especially with all their heart and dedication

With this thank you, we also want to send to all the teachers in Ho Chi Minh City University of Technology, as well as the teachers of the Department of Food Technology - Faculty of Chemical Engineering for their guidance and help to overcome over many difficulties in the process of training and learning during our time at the university

After finishing the thesis, I strong believe that: “Everything will be okay in the end If it's not okay, it's not the end.” —John Lennon

Besides, because of the lack of knowledge and time, this topic is yet to be optimal, hopefully everyone will contribute to improve the study and the application of classification tools

To Phan Chieu Dan

ABSTRACT

Coffee is an important commodity, accounting for 3% of Vietnam’ GDP, and the Vietnam’ export turnover of coffee has reached over US $3 billion for many years In recent years, although the world economy has experienced many difficult times leading to a decrease in purchasing power, Vietnam’s coffee exports have maintained a very encouraging growth rate, there is an unmet need to protect coffee brand and qualify to prevent the frauds in coffee supply chain Thus, this work proposed the suitability of NIR spectroscopy coupled with chemometrics methods for the non- destructive authentication of Geography indicator coffees For this, GI coffees beans (n=49) and non-GI coffees beans (n=103) by directly analysing without any sample preparation Then, PLS-DA and SIMCA were used to construct the models PLS-DA combine with MC, SNV pre-processing constructing model achieved the best result, model accuracy 87.96%, sensitivity 81.25% and specificity 95.23% By the way, SIMCA combine with MC, 2nd Der, SNV pre-processing constructing model achieved the model accuracy just 85.76% Therefore, the proposed methodologies can be useful for both the consumer and regulatory in registration GI for products because it confirms the elevated standard of Dak Lak specialty coffees (GI), preventing fraudulent labeling, friendly with environmental and quick technique, save time and save money

Besides, this study also research about the effective of Geography indicator logo to consumer willing to pay This study conducts on 100 consumers participated in a central location test in Ho Chi Minh city Respondents first rated hedonic liking of a cup of black coffee in a blind condition, then evaluated elements of product concepts differing in two extrinsic attributes: region of origin and label claim, before indicating their liking and purchase intent in an informed condition The packaging claim about region of coffee were the strongest drivers for informed liking of coffee, followed by blind liking and price As the same coffee qualify, talk about extrinsic factor: Geography indicator logo claim is attractive consumer liking, but it’s not the importance factor effective to informed liking and purchase intent Consumer awareness about GI is not high to motivate their willing to pay GI coffee with price

260.000 VND/ kg Thus, in order to enhance the effectiveness of Geography Indicator to consumer awareness, we need more action to educate consumer about what is Geography Indicator and how it effective to coffee qualify and prevent the fraud of coffee

TÓM TẮT LUẬN VĂN

Cà phê là một mặt hàng quan trọng, chiếm 3% GDP của Việt Nam và kim ngạch xuất khẩu cà phê của Việt Nam đã đạt trên 3 tỷ đô la Mỹ trong nhiều năm Trong những năm gần đây, mặc dù kinh tế thế giới trải qua nhiều giai đoạn khó khăn dẫn đến sức mua giảm nhưng xuất khẩu cà phê của Việt Nam vẫn duy trì được tốc độ tăng trưởng đáng khích lệ, do đó để đảm bảo chuỗi cung ứng cà phê cần có biện pháp bảo vệ thương hiệu cà phê và ngăn chặn các hành vi gian lận về chất lượng sản phẩm Nghiên cứu nhằm sử dụng phổ cận hồng ngoại NIR cùng với các phương pháp xử lý số liệu Chemometrics để xác thực và bảo vệ thương hiệu của cà phê đạt chỉ dẫn địa lý (GI) vùng Buôn Mê Thuột Trong nghiên cứu, số mẫu cà phê nhân đạt chuẩn GI (n = 49) và cà phê nhân không đạt chuẩn GI (n = 103) bằng cách phân tích trực tiếp mà không cần phá hủy mẫu Sau đó, PLS-DA và SIMCA được sử dụng để xây dựng các mô hình Mô hình kết hợp PLS-DA với tiền xử lý MC, SNV cho kết quả tốt nhất, độ chính xác của mô hình 87,96%, độ nhạy 81,25% và độ đặc hiệu 95,23% Đồng thời, mô hình kết hợp SIMCA với tiền xử lý MC, 2nd Der, SNV đạt được độ chính xác của mô hình chỉ 85,76% Do đó, các phương pháp đề xuất có thể hữu ích cho cả người tiêu dùng và cơ quan quản lý trong việc đăng ký GI cho sản phẩm vì nó khẳng định tiêu chuẩn chất lượng của cà phê đặc sản Đắk Lắk (GI), ngăn ngừa gian lận nhãn mác, phương pháp thân thiện với môi trường, kỹ thuật nhanh chóng, tiết kiệm thời gian và tiền bạc Nghiên cứu cũng đánh giá ảnh hưởng của hình ảnh logo GI trên bao bì đến ý định mua của người tiêu dùng Nghiên cứu thực hiện trên 100 người, đánh giá tập trung tại Thành phố Hồ Chí Minh Người thử sẽ đánh giá mức độ yêu thích theo thang điểm 5; đầu tiên, là đánh giá cảm quan về chất lượng cà phê đen pha phin, sau đó, đánh giá nội dung trên bao bì, cuối cùng là đánh giá cảm quan sản phẩm khi đã đọc thông tin trên bao bì Kết quả nghiên cứu cho thấy, thông tin xuất sứ của cà phê trên bao bì ảnh hưởng nhiều nhất đến mức độ yêu thích của sản phẩm cà phê, sau đó là chất lượng cảm quan và giá cả của sản phẩm Với cùng chất lượng cà phê, nói về các yếu tố bên ngoài bao bì: thông tin chỉ dẫn địa lý (GI) có tác động tích cực đến mức độ yêu thích của người tiêu dùng, tuy nhiên không phải là yếu tố quan trọng tác động đến ý định mua của người tiêu dung Nhận biết của người tiêu dùng về chỉ

dẫn địa lý là chưa cao, do đó chưa đủ động lực để người tiêu dùng sẵn sàng mua sản phẩm cà phê có chỉ dẫn địa lý với giá 260.000 vnđ/kg Do đó, để tăng giá trị của cà phê có chỉ dẫn địa lý trên thị trường, cần tăng nhận thức của người tiêu dùng về chỉ dẫn địa lý, để từ đó có thể phát triển chất lượng cà phê và tránh giả mạo chất lượng cà phê ở thị trường Việt Nam

DECLARATION OF AUTHORSHIP

I hereby declare that this thesis was carried out by myself under the guidance and supervision of Dr Nguyen Quoc Cuong and Assoc Prof Le Nguyen Doan Duy; and that the work contained and the results in it are true by author and have not violated research ethics The data and figures presented in this thesis are for analysis, comments, and evaluations from various resources by my own work and have been duly acknowledged in the reference part

In addition, other comments, reviews and data used by other authors, and organizations have been acknowledged, and explicitly cited

I will take full responsibility for any fraud detected in my thesis

Ho Chi Minh City, July 01, 2022

To Phan Chieu Dan

1.2 Consumer trend on coffee label 2

1.3 National situation- Our problem 4

1.4 Geography Indicator (GI) 5

1.4.1 Geography Indicator overview 5

1.4.2 GI for Buon Me Thuot Coffee in Vietnam 7

CHAPTER 2: OVERVIEW 11

2.1 Methods for authentication 11

2.2 Infrared spectroscopy 12

2.2.1 Near Infrared spectroscopy (NIR) 13

2.2.2 Advantages & disadvantages of NIR 14

2.3 SCiO handheld NIR device from Consumer Physics 15

2.4 GI coffee authentication model 16

2.4.1 Pre-processing 16

2.4.2 Classification model 17

2.4.2.1 SIMCA classification model- Class modeling method 18

2.4.2.2 PLS-DA classification model- Discriminant method 20

2.4.3 Model validation 21

2.5 Conjoint analysis 23

2.6 Impact of extrinsic characteristics on product evaluation 23

2.7 Effective of GI label on liking and purchase intention 25

2.8 The purpose of study 26

CHAPTER 3: MATERIAL AND METHODS 28

3.2.1 NIR spectra acquisition 31

3.2.2 Research flowchart for building model by NIR spectra 31

3.2.3 Experiment protocol for consumer test 33

3.2.3.1 Blind evaluation of sensory 33

3.2.3.2 Sensory expectation from extrinsic cues by evaluating acceptance of extrinsic attributes 33

3.2.3.3 Combined evaluation of sensory stimulus with extrinsic cues 34

3.2.4 Data analysis 35

3.2.4.1 Pre-processing data (for NIR data) 35

3.2.4.2 Aggregated analysis (for consumer data) 36

CHAPTER 4: RESULTS AND DISCUSSION 38

4.1 Building a model to authenticate GI coffee and non-GI coffee 38

4.1.1 Visualize raw and pre-processed spectral data 38

4.1.2 SIMCA model 39

4.1.2.1 Building model and Internal validation 39

4.1.2.2 Testing SIMCA model by External validation 40

4.1.3 PLS-DA model 42

4.1.3.1 Building model and Internal validation 42

4.1.3.2 Testing PLS-DA model by External validation 43

4.1.4 Discussion 45

4.1.4.1 Discussion about pre-processing performance 45

4.1.4.2 Discussion about the model performance 46

4.2 Evaluation the effectiveness of GI label on purchase intent of consumer 47

4.2.1 Descriptive of dependent and independent variables 48

4.2.2 Informed hedonic liking 48

4.2.3 Purchase intent 48

CHAPTER 5: CONCLUSION 54

SCIENTIFIC WORKS 56

REFERENCES 57

APPENDIX 62

A.Building a model to authenticate GI coffee and non-GI coffee 62

B.Evaluation the effectiveness of GI label on purchase intent of consumer 63

TABLE OF FIGURE

Figure 1.1 Global: coffee, new product launches, by format, 2018-2020………1

Figure 1.2 Global: new coffee launches, by key macro-trends, 2010-2020……….2

Figure 1.3 US selected factors which would encourage coffee purchase,2019………3

Figure 1.4 Environmental and ethical claims dominate new coffee launches………4

Figure 1.5 Logo of Geography Indicator of Buon Me Thuot coffee……… 12

Figure 2.1 IR spectroscopy regions (Mani, Mani & Pro, 2020) ….……… 16

Figure 2.2 SCiO handheld NIR spectrometer (Consumer physics) ……… 17

Figure 2.3 Graphical representation of the general distinction between discriminants (Marini F, 2007) ……….……… ……… 18

Figure 2.4 Top 10 most important coffee purchase drivers, April 2020……….26

Figure 3.1 Collected coffee beans……… 29

Figure 3.2 Research flow chart………35

Figure 4.1 Raw NIR spectra (X0) obtained from handheld Scio NIR device……… 41

Figure 4.2 Pre-processed NIR spectra with a) MC, b) 1st Der +MC, c) 2nd Der + MC, d) SNV +MC, e) SNV+ 1st Der+ MC, f) SNV + 2nd Der + MC……… 42

Figure 4.3 Coomans’ Plot for SIMCA model Z6……….44

Figure 4.4 Predicted scores of test sets belonging to 2 groups……….47

Figure A.1 Data set of NIR spectra……… 62

Figure B.1 Data set of consumer evaluation……….65

TABLE OF TABLE

Table 3.1 General origin information of collected sample……… …… 29

Table 3.2 Sociodemographic sample characteristics……… ……… ….32

Table 3.3 Sensory expectation test for coffee……….…… ……… 37

Table 3.4 Informed test for coffee evaluation……….……….………38

Table 4.1 SIMCA model result by internal validation……….42

Table 4.2 SIMCA model result by external validation………43

Table 4.3 PLS-DA model result by internal validation……… 45

Table 4.4 PLS-DA model recognized test sets………46

Table 4.5 PLS-DA model result by external validation……… 47

Table 4.6 Model performance comparison (external validation) ………50

Table 4.7 Descriptive overview of dependent and independent variables………52

Table 4.8 Seemingly unrelated regression (SUR) of hedonic liking and purchase intent: aggregated results (n = 400 observations) ………52

Table 4.9 Relative importance of sensory and non-sensory characteristics for hedonic liking and purchase intent: aggregated results………53

Table A.1 Data table of NIR spectra……….61

Table B.1 Data table of consumer evaluation……… 63

CHAPTER 1: INTRODUCTION 1.1 Coffee

Vietnam is the world’s second-largest producer of coffee Today, coffee culture is huge in Vietnam, where you can grab coffee for under a buck at the thousands of street stalls located in every city as well as in coffeeshops and restaurants The vast majority of coffee in Vietnam comes from the robusta species, mostly growth in highland Robusta coffee is generally stronger, nuttier, and darker than that made from Arabica

Coffee bean quality was effected by species, method of cultivation and harvest Especially, method of cultivation content: plating soil, topography, climate, etc After harvesting, coffee bean pass through physical and chemical processing like washing, drying, sorting, heating, grinding

Covid-19 accelerated this trend, forcing drinkers to make coffee at home rather than replying on coffee shops In two years time, this trend will be further developed as coffee- making technology improves and machine prices reduce

Figure 1.1 Global: coffee, new product launches, by format, 2018-2020 [17]

Source: Mintel GNDP

Coffee brands are far more sustainable than a decade ago, but future sales reply on them getting more serious still Post-pandemic, consumer anxiety will focus on environmental harm and seek scapegoats A more activist younger generation will show less tolerance for waste, esspecially pods that are recyclable but rarely

Global: coffee, new product launches, by format, 2018-2020

Whole beanGroundPods/capsulesSoloble/instantCoffee mixesRTD (iced) coffee

recycled Covid 19 has made people more sensitive to inequalities, and most farmers are poorly paid despite coffee’s huge profits and use of fair trade claims Brands will need to help many farmers navigate global warming to avoid the loss of supply and livelihoods

Figure 1.2 Global: new coffee launches, by key macro-trends, 2010-2020 [18]

* Health includes the following claim categories: Functional, Plus, Natural and Minus; among food and drink categories

Source: Mintel GNPD

1.2 Consumer trend on coffee label

A growing body of sensory consumer research confirmed that extrinsic product cues, such as packaging and branding, influence how consumers evaluate food products (Deliza & MacFie, 1996) It is important for practitioners and researchers to understand the interplay of sensory and non-sensory attributes as both dimensions have to be optimised for a product to be successful in the marketplace While it is agreed that extrinsic characteristics can both increase and decrease consumer acceptance of a product that is well liked in blind conditions, little is known about the relative effect of extrinsic cues on informed product evaluation when multiple

Global: new coffee launches, by key macro-trends, 2010-2020

Ethical & environmentalHealthConvenience

cues such as branding, labelling, packaging and price are existent Knowledge about their relative importance would guide practitioners to focus on the most important drivers [39]

For example, according to market research, clean-label coffee is now a consumer expectation is developed markets and a trend led by the US More US coffee drinkers would buy coffee making clean/natural claims over those making single-origin or small batch claims US brands such as Caribou Coffee are accentuating how they “have nothing to hide” when it comes to coffee ingredients

Organic provides “proof” of clean label and is especially important to Millenials (b.1978-94) In 2019, 11% of all global coffee launches made the organic claim for the second consecutive year

Figure 1.3 US selected factors which would encourage coffee purchase,2019 [18]

*Defined in this case as North America + Europe + Australia

Base: 1600US internet users aged 18+ who drink any coffee beverage

Source: Lightspeed/Mintel; Mintel GNPD

Coffee was found to be a product for which the evaluation of intrinsic sensory characteristics is strongly impacted by extrinsic attributes [39] It is therefore chosen as an especially suitable product category for this study

No artificalCleaningredients

Environmentallyfrendly (egsustainableingredients)

OrganicSingle originbeans

Small batchroasted

US: selected factors which would encourage coffee purchase, 2019

% of consumers

Figure 1.4 Environmental and ethical claims dominate new coffee launches [17]

Global: coffee, new product launches, top five claims, 2019 Jan-mid December 2019 Source: Mintel GNPD

1.3 National situation- Our problem

Coffee is an important commodity, contributed for 3% of the country's GDP, export turnover is average 3 billion USD per year For developing the export market of this item in the future, branding must be focused Statistically, Vietnam's coffee has been exported to more than 80 countries and territories, occupied for 14.2% of the global green coffee export market share (ranked 2nd, after Brazil) In particular, the exporting roasted, and ground coffee contributed for 9.1% of the market share (ranked 5th, after Brazil, Indonesia, Malaysia and India) Besides, the active support of ministries and branches in processing capacity, the expanding markets and the reorganizing exports along with the initiative and efforts of enterprises Vietnam in promotion, marketing and brand positioning will help Vietnamese coffee products increasingly assert their position in the international market Currently, the orientation of the State and ministries focus on Vietnam's coffee industry to develop in the modern, synchronous, sustainable sand highly competitive direction with diversified, high-quality products, high added- value, increasing income for farmers and businesses [36]

Environmentallyfriendly product

Environmentallyfriendly package

Environmental and ethical claims dominate new coffee launches

% of launches

To achieve the goal of export turnover of 6 billion USD by 2030 and increase the added- value of Vietnamese coffee products, the coffee industry needs to have synchronous solutions:

- Regarding the production and processing, it is necessary to promote the restructuring of the coffee industry effectively

- Building brand must be paid more attention

- Enterprises need to survey the market's demand in areas including market share - taste - quality - price, thereby determining the proportion of processing suitable products

- Regarding to trade promotion, adjust production and business activities in accordance with market signals [36]

Thus, in order to protect the quality of coffee which qualify for standard, bring uniqueness about coffee type, reputation of region of coffee growth, we need to pay more attention to the geographical origin of products through claim about Geography Indicator

1.4 Geography Indicator (GI)

1.4.1 Geography Indicator overview

Geographical indications (GIs) are signs which identify goods that are originating from a specific place and possess a given quality, reputation or other characteristic that is essentially attributable to that geographical origin GIs enable consumers to differentiate products, as they pay increasing attention to the geographical origin of products One of the key benefits of GIs for consumers is therefore to guarantee the quality of the product [15]

GIs can be applied to industrial, agricultural, and handicraft products and encourage diversity in these sectors GIs protect producers against unfair competition and usurpations and adds value to their product by commanding a premium price GIs protect consumers against a misleading description of the origin and the characteristics of the product, and foster national, regional and international trade GIs also support rural development in terms of jobs and higher incomes for

producers and stakeholders of the value chain, and can also promote the region as a whole, with the development of tourism

GIs are a means to preserve traditional knowledge and local biodiversity since products identified by a GI are often the result of traditional processes and knowledge carried forward by a community in a particular region Since the implementation of the World Trade Organization (WTO) Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS Agreement) (1994), GI protection systems, which started in southern Europe, have expanded remarkably worldwide, in particular in Asia Indeed, all countries from the Association of Southeast Asian Nations (ASEAN) have opportunities to develop high-quality products with a strong geographical identity and have strongly engaged in the identification and registration of GIs as a tool to expand their presence on domestic and international markets

As of January 2019, 346 GIs have been registered in ASEAN countries, including 37 for foreign GIs showing the incredible interest of ASEAN countries for GI protection Including both local and foreign GIs, Cambodia has 3 registered GIs, Indonesia 74; Lao PDR 1; Malaysia 84; Thailand 115; and Viet Nam 69 To date, there are eight (08) GIs from the ASEAN region registered in the EU market, including Kampot Pepper (pepper, Cambodia, registered in 2016), Skor Thnot Kampong Speu (sugar, Cambodia, registered in 2019), Kopi Arabika Gayo (coffee, Indonesia, registered in 2017), Nuoc Nam Phu Quoc (fish sauce, Viet Nam, registered in 2012) and four GIs from Thailand, including Khao Hom Mali Thung Kula Rong- Hai (rice, 2013), Kafae Doi Chaang (coffee, 2015), Kafae Doi Tung (coffee, 2015), and Khao Sangyod Muang Phatthalung (rice, 2016)

One of the key benefits of GIs for producers is the increase in the price of the product In the EU, the price of a GI product has been estimated at 2.23 times the price of a comparable non-GI product (in average, 1.5 times more for agro-food products) Another worldwide study estimates that the GI premiums lead to prices 20% to 50% higher than comparable non-GI product In the ASEAN region, according to the data provided by the concerned IP offices in the booklet, GIs show a positive impact in terms of volumes, prices, and local development For example,

for all the GIs for pepper, there has been an increase in prices, during a period where the international price of pepper was relatively stable The price of Kampot white Pepper (Cambodia) increased by a factor of 2.6 between 2009 and 2018, the price of Muntok White Pepper (Indonesia) has increased by a factor of 6 between 2009 and 2015, while the price of Sarawak pepper (Malaysia) increased by a factor of 4.32 from 2003 (before GI registration) to 2016 (after GI registration) for sales in bulk

Other successful GIs are in the area of coffee, with the farm gate price of Flores Bajawa Arabica Coffee red berries (Indonesia) increasing by a factor of 2.2 between 2005 and 2015, although such price increase remains unstable For Doi Chaang Coffee (Thailand), the price of coffee berries evolved by a factor of 2 Buon Ma Thuot coffee from Viet Nam benefits from an added value of 2–3% compared with the standard comparable coffee Fruits also largely benefit from GI protection with the Koh Trung pomelo (Cambodia) farm gate price increasing by a factor of 1.33, and the price of Pakpanang Tabtimsiam Pomelo (Thailand) increasing by a factor of 1.75

Until January 2019, Vietnam has registered 346 GIs in ASEAN, including: Buon Me Thuot coffee (2005), Moc Chau Shan Tuyet Tea (2010), Phu Quoc fish sauce (2012), Son La coffee (2017), Binh Phuoc Cashew (2018), so on

Other key benefits of GIs are the development of the structure of the GI product value chain and the creation of a collective organisation of producers and processors for the management of the GI such as, for example, the Community for the Protection of Geographical Indication of Amed Bali Salt (Indonesia) Agro-tourism, another key benefit, was developed in the Sarawak Pepper (Malaysia) area largely thanks to the GI Coffee festivals have been organised in Buon Ma Thuot (Viet Nam) since the GI registration Finally, the preservation of traditional rice varieties is expected with the GI Khao Kai Noi (Laos)

1.4.2 GI for Buon Me Thuot Coffee in Vietnam

Coffee bean in specific geographical area in Buon Me Thuot was registered GI on 14 October 2005, which has specific characteristics, production, and processing to make uniqueness about sensory [49]

Figure 1.5 Logo of Geography Indicator of Buon Me Thuot coffee

• The main characteristics of coffee bean:- Bean colour: greyish-green, green or light

- Bean size: 10-11 mm long, 6-7 mm wide and 3-4 mm thick - Flavour typical of coffee as being roasted to a suitable level

- Aroma: attractive, typical, with medium to high intensity (typical trait) - Body: average to high (typical trait)

- Acidity: low

- Caffein content: 2.2-2.4% • The method of production:

- Selected varieties belong to the Robusta genetic group Seeds or buds for grafting must be provided by licensed seed production units

- Shade trees: ensuring to prevent at least 20% direct sunlight - Irrigation: supplying enough water during the dry season - Organic fertilization: 10-20 tonnes of manure/ha/year

- Chemical fertilization, plant protection measures, pruning, based on soil analysis and the guideline of technical extension workers

- Harvesting, hand-picked ensuring at least 90% ripened fruits • The method of processing:

- The Buon Me Thuot coffee beans are processed from the fresh fruits of the Robusta coffee tree by the wet (full- washed) or dry (natural) method

• Geographical area:

- Consisting of districts: Cu M’gar, Ea H’leo, Krong Ana, Cu Kuin, Krong Buk, Krong Nang, Krong Pak, Buon Ho town; and Buon Me Thuot City of Daklak

province (Cu Kuin separated from Krong An; Buon Ho separated from Krong Buk)

• Link with the origin:

- Soil for coffee planting: soil type: red- brown basaltic soil Soil depth and slope: depth of the basaltic soil is at least 0.7 m; soil slope a maximum of 15º - Altitude of coffee planting region (above sea level): The coffee trees are planted within an altitude range of 400-800 m This range ensures high diurnal temperature difference in the ripening season that contributes to the high coffee quality

- Temperature and diurnal temperature difference of coffee planting region: Yearly average temperature: 24–26 ºC Diurnal temperature difference in fruit ripening season: above 11.3 ºC

• Geography Indicator management body/association - Buon Ma Thuot Coffee Association

- Maintains the sustainability of coffee production

- Preserves the pride of coffee producers with GI reputation/image - Contributes to local cultural events (coffee festivals, competitions) - Contributes to improving the livelihood of coffee farmers

• GI right infringement

- Buon Ma Thuot Coffee was registered in April 2011 in China by a Chinese trading company as a trademark Following the action from the Buon Ma

Thuot Coffee Association, the trademark registration was cancelled in May 2014

Currently, GI coffee in Viet Nam is not popular in local market, it mainly for exporting, gets a high price (approximate 260.000 VND/kg -280.000 VND/kg) in case normal coffee just from 220.000 VND/kg -250.000 VND/kg, because it made sure about high quality about sensory, brought reputation for Viet Nam; however, it was fronted with fraud about quality, coffee type, fake about geography information Thus, we need to authenticate GI coffee in Buon Me Thuot to normal coffee in Viet Nam, especially is Central Highland

CHAPTER 2: OVERVIEW 2.1 Methods for authentication

In order to protect the quality of coffee, prevent the fraud about type of coffee, the fake about geography information; we need to authenticate high quality coffee to low quality coffee In this case, we need methods to distinguish coffee qualify for GI standard and non- qualify

Nowadays, there are numerous methods available for authentication of coffee, from biological based method of DNA; chemical-biological method of enzymatic and immunological to chemical which based methods of spectroscopy and chromatography In the case of DNA-fingerprint method, an extremely reliable method, using genetic characteristics to discriminate between strains which allows the quantitative determination of the presence of foreign species at low levels Or chromatography such as HPLC and GC-MS, which are also popular amongst authentication methods, has various advantages including high capacity, reproducibility, sensitivity, and versatility Despite different approaches and benefits, two methods above and many current methods share one common characteristic which is laboratory based And being laboratory-based methods still remains some significant drawbacks when they are cumbersome, time consuming, require highly skilled technicians and may include destruction of samples along with substantial use of chemicals [12]

In a competitive world in which global trading plays an important role in any country's economy, acknowledging the limits of laboratory-based methods could cause losses which have urged scientists to come up with more appropriate methods In recent studies, innovative spectroscopic fingerprinting techniques have proved to be an affordable, rapid, chemical free, comprehensive, and especially non- destructive tool for a variety of products Furthermore, combining acquired spectral data with chemometrics, the result is remarkable when providing each unique chemical profile enabling rapid subtle differences configuration Some examples for application in agricultural products can be listed such as authentication of Basmati

rice , coffee bean cultivars [14], green asparagus cultivars [4], fresh versus frozen then thawed beef [37], and measurement of adulteration of olive oils [33]

These researches have strengthened the effectiveness of spectroscopy that if an applicable database is used, the fingerprints examination of authentic samples to mixed or mislabelled samples can tell whether there are adulterations or not within a few minutes, even non-specialist users can carry out the analysis

2.2 Infrared spectroscopy

The general concept of spectroscopy is to obtain information on the structure and properties of matter The basic principle shared by all spectroscopic techniques is to shine a beam of electromagnetic radiation onto a sample and observe how it responds Most common types of spectroscopies used nowadays are atomic, UV-Vis, nuclear magnetic resonance, Raman and infrared

Figure 2.1 IR spectroscopy regions (Mani, Mani & Pro, 2020)

The Infrared (IR) spectroscopy is divided into three sub-regions based on their wavelength: Near Infrared (0.75 µm to 2.5 µm); Mid Infrared (2.5 µm to 20 µm); Far Infrared (20 µm to 100 µm) The information obtained after conducting IR analysis are absorption spectra which is recorded as absorbance or transmittance Reason for these absorptions comes from vibration of molecules under radiation emitted from IR light But there are two conditions for this phenomenal to occur, if they are not met then no absorption can happen First is when IR radiation interacts with molecules undergoing a shift in dipole moment which occurs when separation of charge exists, enables coupling with sinusoidally changing of electromagnetic

field and vibrates with a greater amplitude Second condition is that IR radiation has relevant energy for transition to higher vibrational states Furthermore, substances with different characteristics will have different absorption wavelengths From these conditional and specific interactions of molecules with IR radiation, IR spectra is considered as ‘’fingerprint’’ when each spectral is unique to provide a more specific qualitative information about chemical nature, molecular structure With the condition above, it can be considered that homonuclear diatomic like O2, N2, etc will not be able to absorb IR radiation and cause unwanted spectral when undergoing rotational and vibrational motion because they do not have dipole changing moment On the other hand, carbon dioxide and water (atmospheric and adsorbed) can have negative effects on analysis of weak sample peaks due to strong additional absorption peaks To minimize this, it is essential to regulate concentration of CO2 and H2O during analysis by conducting at a stable condition to determine and eliminate their spectra when handling data [12]

2.2.1 Near Infrared spectroscopy (NIR)

Among IR spectroscopy, NIR with advantages of being sensitive to absorption of food components, quicker response, simpler procedure, and its low instrumentation cost have fixed its position as a future of food authentication

In context of NIR region, spectral are mostly formed by absorption of simple molecular groupings that have strong interatomic bonds such as O-H, N-H, C-H leads to overtones and combination tones of molecular vibrations, these bonds are representative for food characteristic due to their percentages in food component [44]

Following spectral are the main feature of NIR absorption The 1st and 2nd overtones of the fundamental overlapping stretching vibration of O–H and N–H correspond to the NIR bands at 6825 and 1000 nm The 1st, 2nd, and 3rd overtones of the fundamental stretching vibration of C–H are reflected in the NIR at 1780, 1200, and 920 nm The fingerprint regions are represented in NIR spectroscopy as combination overtone bands such as for amide at 2100 nm and for C–H stretching

from 2280 to 2330 nm [34] The overtone bands dominate the NIR spectrum from 1900 nm and include those of O–H and N–H at 1934 nm [26] This occurs partly because the anharmonic constant of an X-H bond is large and partly because the fundamentals of X-H stretching vibrations are of high frequency (short wavelength) [50]

Sample presentation mode when conducting NIR analysis on rice granules is considered as diffuse reflectance mode since incident light is significantly scattered and light scatter is arguably numerically the most important NIR measurements collected This phenomenal is caused by interaction with a variety of angular surfaces from which the light is reflected specularly Specularly-reflected light contains no information about the composition of a sample and may be redirected back along the path of incidence to the detector; scattering increases the intensity of light returning to the detector but also increases the variability of the baseline due to the variable path-length of individual photons of light [14] This effect describes the detection, by diffuse reflectance, of light that is a combination of both absorbed (interaction with the sample) and scattered light (no interaction with the sample) [14] It can have a large influence on the spectrum generated, since the ratio between reflected light (absorbed and scattered) and incident light determines the absorption profile Sample presentation is therefore extremely important in order to minimize light scatter and as far as possible, keep the level of scattering constant for each sample [14], [19] Attempts have been made to develop a mathematical basis to describe light scatter and to accommodate its effects on NIR spectra, but no completely successful strategy has been forthcoming – this has resulted in the study of spectral pre-treatment to address this problem which is discussed deeper in next part [45]

2.2.2 Advantages & disadvantages of NIR

• Advantages of NIR:

- Rapid and simultaneous analysis of multiple samples,

- Non-toxic, environmental friendly by reducing the amount of chemicals used

- Reducing the number of analytical labors, saving costs

- Simple, quick procedure, simplify the sample preparation steps and avoid destroying samples during analysis

- Capable of quantifying one substance in the presence of other substances

- Applicable to both inorganic and organic analyzes

- Available in portable size to enable measurements can be carried out on site

• Disadvantages of NIR

- Low sensitivity of the signal, which can limit the determination of low concentration components with a content of less than 2.5μm (trace) - Require regularly build and update prediction models for each sample

background

- Devices must be continuously calibrated to ensure accuracy - Expensive equipment

2.3 SCiO handheld NIR device from Consumer Physics

Near infrared spectroscopy has been widely used in the horticultural industry as a non-destructive tool to provide quality prediction of fresh and stored products In this work, a low-cost portable NIR sensor, SCiO™ molecular sensor (Consumer Physics Inc., Tel-Aviv, Israel) is assessed for its ability to provide this information Fruit samples of kiwifruit, apple, feijoa, and avocado were collected, and their spectral and quality measurements obtained in order to develop NIR predictive models [30] The performance of the SCiO™ sensor for quality prediction was assessed by developing estimation or classification models using the SCiO™ Lab online application and then compared to that of existing commercial NIR spectrometers A rapid and economic sensor like the SCiO™ would enable wider industrial applicability of the NIR technique and potentially provide fast sorting and screening capability to assist with quality predictions and decision-making processes throughout the supply chain

Principle of this device can be described as follows Light from light source is transmitted through the filter which separates light into near-infrared wavelengths Near-infrared light is then directed onto the product measured at the lamp part as shown in figure 2.2 The reflected light of the product will be captured by the SCiO molecular sensor and taken by an integrated spherical mirror and focused on a detector The spectrum signal from the probe is then processed using the device's calibration model and will be displayed on the screen in digital or spectral form

Figure 2.2 SCiO handheld NIR spectrometer (Consumer physics)[31]

(1) Molecular sensor (2) Light emitter (3) Functional button (On/Off/Calibration)

(4) Battery indicator light (5) USB charging port (6) LED light (7) Calibrator/Cover

2.4 GI coffee authentication model 2.4.1 Pre-processing

Pre-processing mostly aims to get more information from the data without getting fooled by unwanted information by various methods with different application ranges Like any other statistical problems, particularly in spectral data, due to the nature of measurement methods, noise can be baseline creatines, peak shift or drift or one of the most common overlapped peaks These are usually caused by NIR light scattering and FTIR sensitivity to CO2, H2O, the influence of particular size, even measuring conditions such as temperature, humidity etc Depending on which noise or main purpose of treatment, they can be categorized into two main groups:

Smoothing, Correction and Enhancement [5] For Smoothing, we have Golay moving window or K-Neighbor method, which aims to smooth out noise without eliminating valuable information For Correction and Enhancement which mainly focus on enhancing peak, removing baselines or transformation of peak shift and drift such as Derivative, Detrend or removing light scatter such as Multiplicative Scatter Correction (MSC) etc However, some smoothing can be overdone and smooth out some valuable information while correction can enhance too much unwanted noise [11], [28] As each method has its own disadvantages and advantages, the selection of which pre-processing methods will be applied to your data set is still a triggered problem In practice, one tends to combine rather than using one method, this treatment’s strength can cover the others’ weaknesses, for instance, the over enhancement of second derivatives can be improved by applying Savitzky - Golay moving window for some smoothing effect One of the solutions so far is to do a trial-and-error procedure, where different methods will be tested sequentially to see which is the most suitable [38] In summary, all this method's purpose is to increase the Signal to Noise Ratio (SNR), helps us easier to visualize our data, integrate the signal, and above all, helps the main treatment method work at its highest efficiency

Savitzky-2.4.2 Classification model

Before going into detail and the explanation of building model procedure, some of the main data analysis foundation knowledge will be revised There are three main categories for models in statistical analysing, explanatory analysis (e.g., PCA, HCA …), regression analysis (e.g., PCR, PLS Regression, …) and classification analysis (e.g., PLSDA, SIMCA…) In this study, we will focus on Regression and Classification for building authenticity rice models

In the Regression model, our X independent variable(s) will explain for quantitative Y response Linear regression, we have one X predictor explain for one quantitative Y response Furthermore, when there is more than one X predictor

explained for one quantitative Y response, Multiple Regression is adopted, when multiple X predictors explain for more than one quantitative Y response, Multivariate Regression will be applied

But when it comes to the main purpose of predicting quality response, the approaches are referred to as the Classification model [2] The principle behind the classification model is to assign individuals into a group or categorize them by their characteristics From a geometric view, considering each of individuals is a point in a multidimensional space, a classification model will show us which group the individual most likely to belong to According to how each approach finds each class boundary, we can category them into the Discriminant method, finding the class discriminant line and Class modeling method, finding the class region/area [3]

2.4.2.1 SIMCA classification model- Class modeling method

For the Class modeling method, it will focus on finding the similarity among individuals, this means we will gather ones sharing the same characteristics and call it a group Consequently, the region formula for each group will be found by a special algorithm and the response will answer whether they belong to the considered group or not This will result in some individuals appearing in different groups at the same time (sharing common characteristics with different group of individuals at the same time) or even no group at all (doesn’t share the same characteristics to any of the listed groups) since Class modeling only focuses on grouping individuals that have the same characteristics; compared with the Discriminant approach where every individuals belong in a group and the sample space is divided completely into classes and no overlap occurs (Figure 2.4) [3] Soft-Independent Modeling of Class Analogies (SIMCA), Unequal (UNEQ) and Artificial Neural Network (ANN) are some of the examples for Class Modeling methods [37]

Figure 2.3 Graphical representation of the general distinction between discriminants (Marini F, 2007) [2]

Soft-Independent Modeling of Class Analogies (SIMCA) based on the supposition that the natural variability present in objects belonging to the same category as the following equation (1) [50]:

Xg = Tg PgT + Eg (1)

where Xg is the matrix of empirical measurements collected on samples belonging to class g, Tg and Pg are the PC scores and loadings extracted from the PC model, respectively and Eg is the residual matrix

Class assessment is the process of building the model space by calculating the distance d of each object to the class space, summing up the two different contributions: the distance from the model space (orthogonal distance), and the distance into the scores space (scores distance) The orthogonal and score distances are equivalent to Q and T2, which are calculated as the sum of squares of the model residuals and the Mahalanobis distance from the center of the scores space, so the distance of an i-th sample can be calculated as [11]:

di,g2 = T2i,g2 + Qi,g2 (2)

Then, the number of components will be extracted by PC model, once it has been calculated, the data will be projected onto a new T2

new and Qnew Then the individual distance di,g will be calculated based on normalized T2 and Q with 95th percentile under the null hypothesis, equation (2) now becomes:

So finally, the i-th sample will be accepted to the class g if d2

i,g ≤ 2, otherwise it is rejected

2.4.2.2 PLS-DA classification model- Discriminant method

For the Discriminant method, it could be more familiar and easier to visualize since it is quite similar to the basic Regression model These methods focus on how different each assigned group is from each other From that, a mathematical algorithm will be applied to find the discriminant lines (straight or curvy) to split our sample space into classes The response will show you whether our individual belongs to the group on which side of the discriminant lines, means every individual can only belong in one group An example for these methods is Fishers’ linear discriminant analysis [32] or Partial Least Square Discriminant method (PLS-DA), Orthogonal PLS-DA [26] or nonparametric methods like K-Nearest Neighbor [20], Support Vector Machine [24], etc

Partial Least Squares Discriminant Analysis (PLS-DA) is a linear classification tool using the application of Partial Least Square (PLS) Regression Algorithm The main technique behind is to find the optimal number of Latent variables (LVs), the linear combination of the observable variables, known as manifest variables (MVs), which has the maximal covariance with the Y response Consequently, this shows a graphical representation to visualize and understand the pattern of data by latent variable T scores and P loadings P loadings represent the coefficients of the linear combination which determines the LVs and T scores represent coordinates of samples in the LV projection hyperspace [3] The classification problem now can be reduced as a regression equation (3) to find the classification lines formula:

Y = XB + E (3)

where X is the empirical measurements, matrix collected on training samples (n sample x p feature), B is the regression coefficient matrix, Y is the Dummy Matrix (n sample x g group), and E is the residual [3]

The Dummy Matrix will be built in advance This binary matrix has the dimension of n sample x g group, means each individual row represents the individual samples and each column represents class belonging Each entry yig of Y shows whether the i-th sample belongs to the g-th group and encoding by a binary code (0 = no and 1 = yes) For instance, the three-class problem, respectively to Group 1, 2, 3 we will have 3 vectors as y1 y2 and y3 coded as:

2.4.3 Model validation

In building a model, it is necessary to validate the calibration model to measure the predictability of the model As usual, the data set should be divided into two sets: a training set for building the calibration model (mentioned in the previous part) and a testing set for testing the predicting ability of the built model Depending on whether the tested variables are different from those in the calibration model or not, we can classify them into External validation and Internal validation (or Cross Validation) [3]

External validation is when our training set and testing set are separate from each other This helps the remaining set to be completely independent from the former set and the calculation of the predictability capacity is considered more precise when the testing set is relatively unknown from the model [2] However, this required our sample space to be big enough and both sets be representative of the set of samples expected during the routine use of the model Consequently, this included further resampling algorithms to separate sets such as Duplex algorithm [5], or the Kennard–Stone approach [8] This is still one of the simple approaches in validating models, however, there are some potential drawbacks for this The estimation of the test error can be varied depending on the “content” of each set, which observations are included in which set In addition, some statistical methods tend to perform worse when there are fewer observations, this can result in the overestimation of test error when the testing test is usually acquired about 20% of the total data set [7]

Internal Validation or Cross Validation (CV) will address the above issue, the training set and testing set now are not completely separated from each other Depending on the partition method, the data will be differently divided into numbers of subsets Each of the subset takes turn to be left out, the calibration model will be built based on the rest This repeatedly rotates until all subsets have been taken out to validate the model and the error or predict ability will be calculated among all models The most common methods for CV are Leave one out cross validation (LOO-CV) or k-fold cross validation [5]

Flow for evaluation the model: the data set will be split into training and testing set in advance by Duplex algorithm [7] This algorithm helps both of our data sets have the maximum and uniform coverages, avoid over and underestimation For selecting an optimal model, Cross validation will be applied, the training will be divided into k subgroups (each contains N/k samples), then, each time, one subgroup will be left out and the calibration model will be built on the rest Then the subgroups will be introduced back to the built model to calculate some classification parameters This will be repeated until all subgroups have been left out and all final parameters will be calculated average [27] After that, to evaluate the predictability

of the model to unknown samples, External Validation will be applied with the test set

2.5 Conjoint analysis

Traditional sensory analysis, which focused on intrinsic product attributes alone, is not sufficient to meet the requirement of today’s fast-moving markets An optimized product formulation is necessary for a successful innovation; however, consumers are also influenced by extrinsic product information such as brand, price or labelling

Especially psychologists have long been interested in the effects of the combination of sensory stimuli, both intrinsic and extrinsic, in product evaluation Conjoint analysis (CA) is a generic expression for stated preference (SP) experimental approaches whereby consumers respond to product profiles characterized by specific attributes varying at specific levels, according to a statistical design of experiments [25] In essence, this experimental methodology measures product attributes’ impact on consumer preferences

The attributes studied are usually varied according to a factorial design plan and each consumer gives scores, either liking or purchase intent, for a few combinations of the attributes In most cases, different consumer groups respond differently to the attribute combinations In such cases, it is of great importance for the purpose of generating marketing strategies to identify the segments and then to interpret them in terms of demographic or other external information (here called consumer variables) Conjoint analysis has proved to be a great commercial success [25]

2.6 Impact of extrinsic characteristics on product evaluation

Previous research following the expectation disconfirmation framework mainly applied two different strategies to examine the effect of extrinsic attributes on sensory evaluation [40] On one side, the impact of one single extrinsic cue on informed product evaluation was analysed Examples for this approach consist of reporting a strong effect of champagne brands; finding a direct impact of wine price

on liking; analysing the effect of wine closures on product evaluation; and showing how wine critic ratings influence informed liking [20] While, all these studies confirmed a strong influence of the specific single attribute analyzed on informed product evaluation, in realistic settings consumers are not only exposed to one but to multiple extrinsic cues [47]

On the other side, a number of studies measured the combined effect of several extrinsic cues on product evaluation, without aiming to disentangle their relative impact Examples for this approach include analyzing the impact of packaging, juice type, concentration, origin, and vitamin content [11]; measuring the combined effect of price, package and brand for beer [8]; studying wine origins and labels [13] and examining the joint impact of price, brand, packaging and regions for wine [41]

Based on 1653 internet users aged 18+ who drink any coffee beverage at home, they voted top 10 the most important purchase drivers includes both of extrinsic and intrinsic attributes of coffee, such as: roasted type, price, flavour, band, environment, and something relevant to economic protection [18]

Figure 2.4 Top 10 most important coffee purchase drivers, April 2020 [18]

Base: 1,653 internet users aged 18+ who drink any coffee beverage, at home Source: Lightspeed/Mintel

There are a few sensory consumer studies utilising a conjoint analysis approach to separate the relative effect of extrinsic and intrinsic cues on consumer choice [17], [24] But all of them were limited to one single intrinsic attribute, such as sweetness or aroma, to avoid the interaction of multiple sensory cues