Nghiên cứu phát triển màng bảo quản từ pectin kết hợp cao chiết vỏ bưởi da xanh (citrus maxima burm merr )

BỘ GIÁO DỤC VÀ ĐÀO TẠO VIỆN HÀN LÂM KHOA HỌC VÀ CÔNG NGHỆ VIỆT NAM HỌC VIỆN KHOA HỌC VÀ CÔNG NGHỆ - Trần Thị Yến Nhi NGHIÊN CỨU PHÁT TRIỂN MÀNG BẢO QUẢN TỪ PECTIN KẾT HỢP CAO CHIẾT VỎ BƯỞI DA XANH (CITRUS MAXIMA BURM MERR.) LUẬN VĂN THẠC SĨ HOÁ HỌC Thành phố Hồ Chí Minh - 2021 BỘ GIÁO DỤC VÀ ĐÀO TẠO VIỆN HÀN LÂM KHOA HỌC VÀ CÔNG NGHỆ VIỆT NAM HỌC VIỆN KHOA HỌC VÀ CÔNG NGHỆ - Trần Thị Yến Nhi NGHIÊN CỨU PHÁT TRIỂN MÀNG BẢO QUẢN TỪ PECTIN KẾT HỢP CAO CHIẾT VỎ BƯỞI DA XANH (CITRUS MAXIMA BURM MERR.) Chuyên ngành: Hóa hữu Mã số: 8440414 LUẬN VĂN THẠC SĨ HOÁ HỌC NGƯỜI HƯỚNG DẪN KHOA HỌC: Hướng dẫn 1: PGS.TS Bạch Long Giang Hướng dẫn 2: PGS.TS Trần Ngọc Quyển Thành phố Hồ Chí Minh - 2021 LỜI CAM ĐOAN Tơi xin cam kết cơng trình nghiên cứu riêng tơi, thực phịng thí nghiệm Viện Khoa học Mơi trường, Trường Đại học Nguyễn Tất Thành hướng dẫn PGS.TS Bạch Long Giang PGS.TS Trần Ngọc Quyển Các số liệu kết nêu luận văn trung thực xác, ý tưởng tham khảo, so sánh với kết từ cơng trình khác trích dẫn luận văn TP.HCM, ngày tháng năm 2021 Trần Thị Yến Nhi LỜI CẢM ƠN Sau năm học tập cao học Học viện Khoa học Công nghệ - Viện Hàn lâm Khoa học Cơng nghệ Việt Nam, đến tơi hồn thành chương trình học tập Để hồn thành luận văn thạc sĩ này, xin chân thành bày tỏ lời cảm ơn đến Học viện Khoa học Công nghệ, Viện Hàn Lâm Khoa học Công nghệ Việt Nam Q Thầy Cơ Khoa Hóa học Đặc biệt hơn, xin gửi lời cảm ơn chân thành đến hướng dẫn khoa học tôi, PGS.TS Bạch Long Giang (Trường Đại học Nguyễn Tất Thành) PGS.TS Trần Ngọc Quyển (Viện Khoa học Vật liệu Ứng dụng) – Người Thầy định hướng, trực tiếp dẫn dắt bảo cho suốt thời gian học tập, thực đề tài nghiên cứu khoa học Bên cạnh đó, tơi xin cảm ơn hợp tác từ cộng sự, anh, chị, em đồng nghiệp Viện Khoa học Môi trường NTT, đơn vị phối hợp, bạn sinh viên đến từ trường Đại học Nguyễn Tất Thành, Đại học Nông Lâm TPHCM giúp tơi hồn thành tốt luận văn Cuối cùng, tơi xin chân thành cảm ơn Tập đồn Vingroup - Cơng ty CP hỗ trợ Chương trình học bổng đào tạo Thạc sĩ, Tiến sĩ nước Quỹ Đổi sáng tạo Vingroup (VINIF), Viện Nghiên cứu Dữ liệu lớn (VINBIGDATA) cho luận văn thạc sĩ DANH MỤC CÁC KÝ HIỆU VÀ CHỮ VIẾT TẮT Kí hiệu Tiếng Anh AA Ascorbic Ac ABTS Antioxidatio Azino-bis(3- 6-sulfonic ac Bx ºBrix CA Contact ang CE Catechin Eq CFU Colony Form CT Control DPPH Free-radical by 2,2-Diph DW Dried weigh GAE Gallic acid e HPLC MFC High-perform chromatogra Minimal pro MW Microwave PFE Pomelo’s fla PPO Polyphenol PT Pectin SR Sprayed TAM Total aerobic TCD Total colour TFC Total flavon TPC Total phenol TSS Total soluble TYM Total yeast, UT Ultrasound WW Wet weight DANH MỤC CÁC BẢNG Bảng Danh sách thiết bị 26 Bảng 2 Danh sách hóa chất sử dụng 27 Bảng Đặc điểm phân bố tỷ lệ bưởi da xanh 34 Bảng Tính chất hóa lý phận bưởi da xanh .35 Bảng 3 Ảnh hưởng yếu tố trình ngâm chiết đến hàm lượng hợp chất có hoạt tính sinh học 38 Bảng Ảnh hưởng yếu tố q trình chiết có hỗ trợ siêu âm đến hàm lượng hợp chất có hoạt tính sinh học .42 Bảng Ảnh hưởng yếu tố q trình chiết có hỗ trợ vi sóng đến hàm lượng hợp chất có hoạt tính sinh học 43 Bảng Ảnh hưởng yếu tố q trình chiết hồn lưu Soxhlet đến hàm lượng hợp chất có hoạt tính sinh học .46 Bảng Định tính hóa thực vật cao chiết vỏ bưởi .49 Bảng Hàm lượng naringin hesperidin cao chiết vỏ bưởi 51 Bảng Đường kính vịng kháng khuẩn cao chiết vỏ bưởi 52 Bảng 10 Độ trương, độ hoà tan hàm lượng nước màng PT kết hợp glycerol 54 Bảng 11 Tính chất màng PT kết hợp với glycerol nồng độ từ 0-1% 55 Bảng 12 Ảnh hưởng tỷ lệ PFE lên tính lý màng PT 59 Bảng 13 Các dao động đặc trưng liên kết PT; PFE; CT màng PT với nồng độ khác 62 Bảng 14 Các đặc tính múi mít tươi (CT) sau phun màng (SR) trước bảo quản .66 Bảng 15 Sự thay đổi tính chất mít Thái MFC theo thời gian bảo quản nhiệt độ phòng .67 Bảng 16 Thay đổi trước sau bảo quản hai mức nhiệt độ 71 Bảng 17 So sánh hiệu bảo quản màng đề tài với nghiên cứu khác 72 DANH MỤC CÁC HÌNH VẼ, ĐỒ THỊ Hình 1 Nguồn gốc polymers sinh học 10 Hình Cấu trúc đặc trưng nhóm có múi 13 Hình Nhóm flavonoid trái có múi 15 Hình Cấu trúc chuỗi pectin (C6H10O7) 18 Hình Bố trí thí nghiệm cho đánh giá nguyên liệu sơ 24 Hình 2 Bố trí thí nghiệm cho q trình tách chiết phương pháp 25 Hình Đánh giá đặc điểm thành phần cao chiết vỏ bưởi .25 Hình Bố trí thí nghiệm khảo sát nồng độ glycerol 25 Hình Khảo sát nồng độ cao chiết PFE lên tính chất màng 26 Hình Bố trí thí nghiệm ảnh hưởng PFE/PT lên mít MFC 26 Hình Quy trình chế tạo màng ăn từ pectin dự kiến 28 Hình Ảnh hưởng phương pháp chiết lên hiệu thu hồi thành phần có hoạt tính sinh học dịch chiết 48 Hình Phổ sắc kí lỏng HPLC cao chiết vỏ bưởi da xanh 50 Hình 3 Quy trình chế tạo màng PT kết hợp glycerol 53 Hình Phổ FT-IR màng PT-glycerol 57 Hình Phổ FTIR PT; PFE; màng PFE/PT 61 Hình Kính hiển vi điện tử quét (SEM) a) màng 0,75% gly/PT; b) 3,2 gPFE/g PT; c) 3,4 gPFE/g PT; d) 3,6 gPFE/g PT 63 Hình Góc tiếp xúc màng PFE/PT a) CT; b) Tỷ lệ 3,2 g PFE/g PT; c) Tỷ lệ 3,4 g PFE/g PT; d) Tỷ lệ 3,6 g PFE/g PT 63 Hình Hình thái A) bề mặt B) bề dày màng 3,4 g PFE/g PT 64 Hình Ảnh hưởng bảo quản nhiệt độ thấp đến độ giảm khối lượng mít CT SR 69 Hình 10 Ảnh hưởng thời gian bảo quản lên chênh lệch màu hệ Lab* múi mít thái A) không xử lý CT; B) Phun màng PFE:PT (SR) 69 Hình 11 Sự thay đổi TAM TYM mít Thái MFC thời gian bảo quản nhiệt độ thấp 70 MỤC LỤC LỜI CAM ĐOAN LỜI CẢM ƠN DANH MỤC CÁC KÝ HIỆU VÀ CHỮ VIẾT TẮT DANH MỤC CÁC BẢNG DANH MỤC CÁC HÌNH VẼ, ĐỒ THỊ MỞ ĐẦU CHƯƠNG TỔNG QUAN 1.1 BẢO QUẢN NÔNG SẢN SAU THU HOẠCH 1.1.1 Tác nhân ảnh hưởng đến chất lượng nông sản sau thu hoạch 1.1.1.1 Nhiệt độ 1.1.1.2 Thành phần chất khí 1.1.1.3 Độ ẩm tương đối 1.1.2 Phương pháp bảo quản 1.1.2.1 Phương pháp vật lý 1.1.2.2 Sử dụng bao bì 1.1.2.3 Phương pháp chiếu xạ 1.1.2.4 Phương pháp hóa học 1.1.2.5 Phương pháp sinh học 1.2 TỔNG QUAN MÀNG 1.2.1 Polymer tổng hợp 1.2.2 Polymer sinh học 1.3 BƯỞI VÀ DỊCH CHIẾT TỪ BƯỞI 1.3.1 Bưởi 1.3.2 Lợi ích sức khỏe 1.4 SỰ THAY ĐỔI TÍNH CHẤT MÀNG KHI BỔ SUNG HOẠT TÍNH KHÁNG KHUẨN 1.4.1 Khả thẩm thấu 1.4.2 Tính lý, vật lý 1.4.3 Tính chất nhiệt 1.4.4 Tính chất quang 1.4.5 Tính kháng oxy hóa 1.4.6 Tính kháng khuẩn 1.5 PECTIN 1.6 NGHIÊN CỨU TRONG ỨNG DỤNG MÀNG SINH HỌC 1.6.1 Ngoài nước 1.6.2 Trong nước CHƯƠNG NGUYÊN VẬT LIỆU VÀ PHƯƠNG PHÁP NGHIÊN CỨU 24 2.1 MỤC TIÊU NGHIÊN CỨU 2.2 NỘI DUNG NGHIÊN CỨU 2.3 DỤNG CỤ, THIẾT BỊ VÀ HÓA CHẤT THÍ NGHIỆM 2.3.1 Thiết bị 2.3.2 Hoá chất 2.5 PHƯƠNG PHÁP NGHIÊN CỨU 2.4.1 Phương pháp tách chiết dịch từ vỏ bưởi 2.4.2 Phương pháp chế tạo màng từ pectin kết hợp với dịch chiết vỏ bưởi dự kiến 2.4.3 Phương pháp đánh giá chất lượng 2.4.3.1 Phương pháp xác định hàm lượng nước, độ hòa tan độ trương màng 2.4.3.2 Phương pháp xác định hàm lượng polyphenol 2.4.3.3 Phương pháp xác định hoạt tính kháng oxy hóa dịch chiết màng ăn DPPH ABTS 2.4.3.4 Phương pháp xác định hàm lượng ascorbic acid 2.4.3.5 Phương pháp xác định hoạt tính kháng khuẩn 2.4.3.6 Phương pháp xác định tính chất lý màng 2.4.3.7 Phương pháp HPLC 2.4.3.8 Phương pháp phun màng 2.4.3.9 Định tính hố thực vật 2.4.3.10 Đánh giá TAM TYM 2.4.3.11 Phương pháp xử lý số liệu CHƯƠNG KẾT QUẢ VÀ THẢO LUẬN 3.1 KHẢO SÁT NGUỒN NGUYÊN LIỆU ĐẦU VÀO 3.1.1 Khảo sát hình thái, đặc tính ngun liệu bưởi da xanh 3.1.2 Khảo sát quy trình tách chiết đặc dịch chiết từ vỏ bưởi 3.1.2.1 Ảnh hưởng trình ngâm chiết 3.1.2.2 Ảnh hưởng q trình chiết có hỗ trợ sóng siêu âm 3.1.2.3 Ảnh hưởng q trình chiết có hỗ trợ vi sóng 3.1.2.4 Ảnh hưởng q trình chiết hồn lưu Soxhlet q% ¼ In which, q1, exp (g.kg 1): The amount of essential oil obtained at equilibrium; i.e it is the amount of essential oil distilled off until reaching saturation q1, cal (g.kg 1): The amount of essential oil obtained from the model by the software; q1, mean (g.kg 1): The average value of q1; N: the number of data points The kinetics of the essential oil extraction from pomelo materials were fitted on the linear and nonlinear forms of firstorder kinetics (Table 1) and second-order kinetics (Table 2) Origin software was used to determine kinetic parameters and predict essential oil yield at the saturation point, q Both of which could be calculated from the graph q t versus t, as shown in Table Similarly, k2 and q of the lin- P.T Dao et al Fig Experimental data and nonlinear form of first order kinetic model: (A): equation 6; (B): equation 7; (C): equation Fig Experimental data and nonlinear form of first-order kinetic model (Equation 9) and second-order kinetic model (Equation 14) ear equation are obtained from the plots of t/q t vs t, 1/qt vs 1/ t, qt vs qt/t, and qt/t vs qt, as illustrated in Figs 2-5 Figs 2-5 shows experimental data of linear and nonlinear equations of the kinetic models that describe extraction pro-cess of pomelo essential oil Examination of the Fig 3A and 3B (Eq 10 and 11) revealed that the experimental data points seemed to be distributed on a straight line, while the distribu-tion of data of Fig 3C and D tended to follow a curved shape, implying that the two equations 12 and 13 are not appropriate to describe the experimental data Regarding the nonlinear forms of equations 6– 9, it is found that the experimental points are consistent with the estimated curves and that they are visu-ally indiscernible However, plotting them along with the nonlinear-pseudo-secondorder model (Eq 14) revealed that nonlinear-pseudo-second-order model is more steep (Fig 5) Kinetic parameters of the models included: the amount of essential oil calculated in the state equilibrium, q 1, the essen- tial oil fraction extracted through the washing, unhindered diffusion and hindered diffusion (fw, fd1, fd2, respectively) and the rate constants for the washing, unhindered diffusion and the hindered diffusion process (kw, kd1, kd2, respectively) The estimated parameters of all models are listed in Table The R2 has been a commonly used indicator kinetic studies to deter-mine the relationship between experimental data and data from the model However, almost all 11 models achieved very high R 2, at higher than 0.9, and are thus suitable to describe the extraction kinetics Therefore, the %q value, which is the difference between experimental and calculated q1, is used as the next evaluation factor for the selection of the appropri-ate model From values of R2 and % q, it was shown that the second order kinetic models (Eq 10–13) fitted the experimental data better than the first order kinetics (Eq 4–5) This result is mosly due to the higher error value in the linear model pseudo first order models (Eq 4–5), of 67.63 and 67.64%, respectively These results show that % q is also a good supporting factor in assessing the suitability of the model Regarding pseudo first order equations in linear form (Eq 4– 5), the equation taking the natural logarithm form (Eq 4) had a higher coefficient of R2 , at 0.91063, then the common logarithm counterpart This could be due to the switching from factor e to factor 10 of the logarithm, leading to the dif-ference of the equation The nonlinear forms of the pseudo first order (equation 6–9) shows higher R2 values and lower %q than the linear form (equation 4–5), suggesting the suit-ability of the former in describing the experimental data The values of q(1, cal) and %q of the nonlinear pseudo second order model (equation 14) are 18.608 and 31.65%, respec-tively These results show that the conversion of nonlinear kinetic equations to linear can alter their error distribution and the kinetic parameters Highest % q values were observed in pseudo first order (Equation and 5), linear and non-linear pseudo second order (Eq 10–14) models, suggesting that those models are not consistent with the experimental data The explanation for this could be two-fold First, because each kinetic model has cer-tain assumptions, the linearization of the curved kinetic to a Kinetics of pilot-scale essential oil extraction from pomelo (Citrus maxima) peels Fig Table Chromatogram of essential oil of pomelo peel Volatile constituents of pomelo peel oil No straight line might have violated the error variance, thus inflat-ing the calculated oil yield at the saturation point [23,24] Second, the non-linear kinetics are capable of fitting the data to suit different mechanisms more flexibly, leading to smaller differences between calculated and experimental data This result is corroborated by a previous study where the equation have been found suitable to describe essential oil extraction kinetics of different materials including cinnamon, lavender and citronella [25] A similar observation was found by other studies suggesting that non-linear fitting is appropriate for describing kinetics of essential oil extraction and that lineariza-tion equations seem to produce errors, leading to violation of model kinetic theories [13,16,26] With the above data, it is more reasonable and reliable to explain the kinetic data through the nonlinear regression method and the first order kinetic model of the nonlinear form Equation (7) has been found to be suitable to describe the mechanism of the extrac-tion of essential oils from pomelo peels Selection of the equation for describing the hydrodistillation process suggests that the transport mechanism of the extraction from pomelo peels initiated as an instantaneous washing, followed by diffusion In which, the former is characterized by the rapid washing of essential oils from the inside to the outside of the material surface while the latter implies slow diffusion of essential oil from the plant tissues outward, which was then washed away by the steam The diffusion phase is represented by a slow increase in the amount of the essential oil during distillation [12,27] Kinetic model of pilot scale pomelo oil extraction under the instantaneous washing mechanism followed by diffusion is calculated as follows: q t q1 In which, qt is the amount of essential oil obtained in the raw material up to time t (g.kg 1), q1 is the amount of essen-tial oil obtained until saturation (g.kg 1) 3.2 Volatile composition of the obtained essential oils Gas chromatography mass spectrometry (GC–MS) was used to analyze the chemical composition of pomelo essential oil The composition is determined by comparing peak data with the NIST data library by their retention times and mass spec-trometry The results are displayed as in Fig and Table Overall, six compounds were identified in the volatile composition, accounting for almost 100% of total content As indicated by the two peaks (11.716 and 9.792) in the chromatogram, the two main components included D-limonene (97.4%) and bMycrene (1.233%) The composition of the remaining four compounds ranged from 0.072 to 0.692% This similarity is also observed in another study where the essential oils were extracted from same materials [28], showing abundance of Limonene (97.1%), b-Mycrene (1.3%) and a-Pinene (0.7%) in its composition In addition, pomelo peel (Citrus grandis Osbeck) of Kenyan origin also showed predominance of Limonene (94.8%), followed by a-terpinene (1.8%) and a-pinene (0.5%) [4] However, in essen-tial oil extracted from Chinese pomelo (Citrus maxima) fruit by steam distillation showed a lower limonene content of 46.83%, followed by b-Caryophyllene epoxide (20.17%) [29] The abundance of volatile compounds in the essential oil can be attributed to the use of different extraction techniques In addition, the composition of essential oils depends on growing conditions and harvesting time [30] Conclusions This study examines the suitability of the different forms of linear and nonlinear pseudo first order kinetic and the pseudo second order models in describing the extraction process of essential oil from pomelo peel material Different kinetic parameters were estimated corresponding to those models and evaluated based on R2 and the difference between calcu-lated and experimental extraction yield at the saturation point Overall, the linear models were found unsuitable to describe the process, probably because of inflated error distribution when linearizing the nonlinear models The non-linear first order kinetic model (instantaneous washing, followed by diffu-sion model) is considered the most suitable for describing the pilot-scale pomelo oil extraction mechanism The advantage of nonlinear equations is that they eliminate the need to know q value at the time of saturation before fitting the experimental points In addition, obtained essential oil was analyzed for chemical composition with DLimonene accounting for 97.4% of total content Current result confirmed the quality of Vietnamese pomelo essential oils and suggested its commer-cial possibilities and potentials Funding Tan Phat Dao was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Pro-gramme of Vingroup Innovation Foundation (VINIF), Vin-group Big Data Institute (VINBIGDATA), code VINIF.2020.ThS.11 Availability of data and material: Not applicable Code availability: Not applicable Author’s contributions: Ethics approval: Not applicable Consent to partipate: Not applicable Consent for publication: Not applicable Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgments Tan Phat Dao was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Pro-gramme of Vingroup Innovation Foundation (VINIF), Vin-group Big Data Institute 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Vacuum Concentration by Response Surface Methodology in Pilot scale To cite this article: T Y N Tran et al 2021 IOP Conf Ser.: Mater Sci Eng 1092 012075 View the article online for updates and enhancements This content was downloaded from IP address 203.167.11.154 on 16/03/2021 at 10:55 iCITES 2020 IOP Conf Series: Materials Science and Engineering Optimization of Pomelo juice Citrus maxima (Burm.Merr.) Vacuum Concentration by Response Surface Methodology in Pilot scale T Y N Tran1,2,* T P Dao1,2,* P T N Nguyen1,3 T N Pham1, B L Huynh4, H C Mai,2 X P Huynh5, T T Tran5 NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Vietnam Academy of Science and Technology Department of Chemical Technology, Nong Lam University, Ho Chi Minh City, Viet Nam Department of Chemical Engineering, University of Food Industry, Ho Chi Minh City College of Agriculture, Can Tho University, Can Tho City, Vietnam Corresponding author: ttynhi@ntt.edu.vn, dtphat@ntt.edu.vn Abstract Because of its healthful nutritional properties, pomelo is gaining traction in the food and beverage industry Pomelo juice concentration as a beverage provides vitamins, antioxidants, and energy The Design Expert 11 calculation software is used to optimize the concentration process parameters, the temperature range is determined to include ± α values converted as axial The results showed a positive correlation between the two factor variables and the obtained target function (TPC), the optimal value for the selected procedure was based on 79 ℃ with a time of more than 1.78 hours At this point, TPC retention is 71.121% Introduction The natural compounds are concerned, leading to the development of food processing technology to serve the needs and health benefits of consumers and solve the problems of supply and demand in the market [1], [2] Pomelo is a fruit tree of the citrus family, with scientific name Citrus maxima (Burm.Merr.) belonging to the Citrus group in the Rutaceace family Originated in Southeast Asia (most in Thailand and Malaysia) [3] Health-promoting compounds, typically total polyphenol content (TPC), zeaxanthin, β-cryptoxanthin, and lycopene are mentioned in the pomelo pupl [4] Vacuum concentrating is used to increase the retention of these nutritional compounds without processing However, publication in pilot sale on pomelo juice concentration (PJC) is still limited At the same time, the RSM method optimally handles the parameters affecting the concentrate process according to Central composite design (CCD) were selected In the past, this method has been published extensively in studies of extracting essential oils and anthocyanins in plants [5]– [9], show reliability and effectiveness The result obtained from the algorithm processing of this method is the optimal constant variable for the response function Therefore, the goal of the research is to interact with concentration temperature (temp) and time in retening TPC for pomelo juice using RSM to make the optimal choice for the vacuum concentrate process pilot scale, to provide a database for the transition to industrial production Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI Published under licence by IOP Publishing Ltd iCITES 2020 IOP Conf Series: Materials Science and Engineering Materials and method 2.1 Preparations of sample Pomelo were purchased in Ben Tre province, Vietnam Selected fruits were ripened, fresh, unspoiled, and undamaged with the weight of around to kg Fruits were pre-treated by washing with water After pre-treatment, the peel and seed were removed The pulp was squeezed with equipment (model MJ-68MWRA, Panasonic, Malaysia) in minute Pomelo juice is adjusted to Brix of 20 from 60°Brix sugar syrup up three litters 2.2 Chemicals The 60°Brix syrup is prepared from making crystal sugar purchased from Bien Hoa Sugar Company, Dong Nai Province, Vietnam Folin-Ciocalteu (FCR), Gallic Acid reagent was purchased at SigmaAldrich and Chemie, Co Ltd (USA) Other chemicals such as distilled water (pH from 6.5 to 8), methanol (purity 99.5%), Na2CO3 (purity 99.5%), NaHCO3 (purity 99.5%) were sourced from China 2.3 Experimental design Response surface methodology, in conjunction with CCD, was employed to optimize the extraction process by generating a set of experimental trials A calculation of experimental trials and optimum yield was performed using Design Expert 11 A central composite design approach was adopted incorporating two variables factor (time and temperature concentration) and one response (TPC) The final set consists of 13 with center points as shown in Table-1 Table Matrix for variables Code Ind A B Conce Conce 2.4 Determind of TPC The treatment sample is then filtered through Whatman No.1 paper and determine TPC by the Folin– Ciocalteu method (Waterhouse, 2002 [10], adjusted by Silva et al [11] Extracts (100µl-dilution ratio 1: 4) were mixed with 500µl of Folin–Ciocalteu reagent, 400µl of 7.5% (w/v) sodium carbonate solution Absorbance at 760nm was measured after 1h, using a spectrophotometer Results were expressed as gram of gallic acid retention in sample (%) 2.5 Equipments The equipment was designed in Gold Quality CO.,Ltd Figure Single cycle vacuum Concentrator (1) Feed valve; (2) Tank; (3) Stirring motor; (4) Cooling system; (5) Condenser water tank; (6) Vacuum pump; (7) Thermostatic tank; (8) Control panel; (9) Exhaust valve iCITES 2020 IOP Conf Series: Materials Science and Engineering 2.6 Statistical analysis Each experiment was triplicated MS software (Microsoft Inc., Redmond, WA, USA) software support and average calculation Combined ANOVA processing by Design-Expert statistical software version 11 (DE11) The optimal concentrate parameter of pomelo juice predicted with significance level below 5% Results and discussion 3.1 Experimental design Two main factors affecting the concentration process of pomelo juice have been determined from the single-factor experiment results, namely concentration temperature and concentration time Next, the RSM surface response method is applied to optimize the total polyphenol content recovered from the process Based on the central complex design (CCD), the quadratic model representing the relationship between the designed inputs with three levels as shown in Table The polyphenol content is closely dependent on the concentration temperature factor Experimental values from DE 11 design are presented in Table Polyphenol content received is the most 72,134 (%) (std1) and the lowest 40.0111 (%) (std 6) Table Experimental design for factors Std Runs order order 10 11 12 13 To determine the importance of each coefficient, the value of F– value and "Prob.> F" (pvalue) was calculated through the ANOVA data processing software Table presents the ANOVA results of the model with the statistical results of each factor Table Analysis of variables Source Model A-Temperature B-Time AB A² B² Residual Lack of Fit Pure Error Cor Total Significant p < 0.05, not significant p > 0.05 iCITES 2020 IOP Conf Series: Materials Science and Engineering From Table ANOVA, the F-value is 13.97 which shows the design model is statistically significant Of which, with the p-value 0.0016, the model shows that only a 0.16% chance of the F-value can occur due to noise The p value 10%, the accuracy can be reduced When designing, it is necessary to remove the factors with p value > 10% to improve the statistical significance of the model Specifically, variables A, B, and A2 have p value