(LUẬN văn THẠC sĩ) 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 download by : skknchat@gmail.com 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 download by : skknchat@gmail.com LỜI CAM ĐOAN Tơi xin cam kết cơng trình nghiên cứu riêng tơi, thực phịng thí nghiệm Viện Khoa học Môi 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 download by : skknchat@gmail.com LỜI CẢM ƠN Sau năm học tập cao học Học viện Khoa học Công nghệ - Viện Hàn lâm Khoa học Công nghệ Việt Nam, đến 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 Quý Thầy Cô Khoa Hóa học Đặc biệt hơn, tơi 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, 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 download by : skknchat@gmail.com DANH MỤC CÁC KÝ HIỆU VÀ CHỮ VIẾT TẮT Kí hiệu Tiếng Anh AA Ascorbic Acid ABTS Bx CA CE CFU CT DPPH DW GAE HPLC MFC MW PFE PPO PT SR TAM TCD TFC TPC TSS TYM UT WW Tiếng Việt Acid ascorbic Khả kháng oxy hoá Antioxidation activity by (2,2′bằng muối 2,2′-Azino-bis(3Azino-bis(3-thylbenzothiazolinethylbenzothiazoline-66-sulfonic acid) diammonium salt) sulfonic acid) diammonium ºBrix Đơn vị TSS Contact angle Góc tiếp xúc Catechin Equivalents Tương đương Catechin Colony Forming Unit Đơn vị hình thành tế bào Control Đối chứng Khả bắt gốc tự Free-radicals scavenging activity 2,2-Diphenyl-1by 2,2-Diphenyl-1-picrylhydrazyl picrylhydrazyl Dried weight Khối lượng khô Gallic acid equivalent Tương đương acid gallic High-performance liquid Sắc ký lỏng hiệu cao chromatography Minimal processing of fresh-cut chế biến tối thiểu Microwave Vi sóng Cao chiết vỏ xanh bưởi da Pomelo’s flavedo extraction xanh Polyphenol oxydase Enzyme Polyphenol oxydase Pectin Pectin Sprayed Sau phun màng Total aerobic microorganisms Tổng số vi sinh vật hiếu khí Total colour difference Tổng số chênh lệch màu sắc Total flavonoid content Tổng hàm lượng flavonoid Total phenolics content Tổng hàm lượng phenolic Total soluble solids Tổng chất rắn hoà tan Total yeast, mold Tổng số nấm men, nấm mốc Ultrasound Siêu âm Wet weight Khối lượng ướt download by : skknchat@gmail.com 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 download by : skknchat@gmail.com 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 download by : skknchat@gmail.com 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 10 1.2 TỔNG QUAN MÀNG 10 1.2.1 Polymer tổng hợp 11 1.2.2 Polymer sinh học 11 1.3 BƯỞI VÀ DỊCH CHIẾT TỪ BƯỞI 12 1.3.1 Bưởi 12 1.3.2 Lợi ích sức khỏe 14 1.4 SỰ THAY ĐỔI TÍNH CHẤT MÀNG KHI BỔ SUNG HOẠT TÍNH KHÁNG KHUẨN 15 1.4.1 Khả thẩm thấu 16 1.4.2 Tính lý, vật lý 16 1.4.3 Tính chất nhiệt 17 1.4.4 Tính chất quang 17 1.4.5 Tính kháng oxy hóa 18 1.4.6 Tính kháng khuẩn 18 1.5 PECTIN 18 1.6 NGHIÊN CỨU TRONG ỨNG DỤNG MÀNG SINH HỌC 19 download by : skknchat@gmail.com 1.6.1 Ngoài nước 19 1.6.2 Trong nước 22 CHƯƠNG NGUYÊN VẬT LIỆU VÀ PHƯƠNG PHÁP NGHIÊN CỨU24 2.1 MỤC TIÊU NGHIÊN CỨU 24 2.2 NỘI DUNG NGHIÊN CỨU 24 2.3 DỤNG CỤ, THIẾT BỊ VÀ HĨA CHẤT THÍ NGHIỆM 26 2.3.1 Thiết bị 26 2.3.2 Hoá chất 27 2.5 PHƯƠNG PHÁP NGHIÊN CỨU 27 2.4.1 Phương pháp tách chiết dịch từ vỏ bưởi 27 2.4.2 Phương pháp chế tạo màng từ pectin kết hợp với dịch chiết vỏ bưởi dự kiến 28 2.4.3 Phương pháp đánh giá chất lượng 29 2.4.3.1 Phương pháp xác định hàm lượng nước, độ hòa tan độ trương màng 29 2.4.3.2 Phương pháp xác định hàm lượng polyphenol 29 2.4.3.3 Phương pháp xác định hoạt tính kháng oxy hóa dịch chiết màng ăn DPPH ABTS 29 2.4.3.4 Phương pháp xác định hàm lượng ascorbic acid 30 2.4.3.5 Phương pháp xác định hoạt tính kháng khuẩn 30 2.4.3.6 Phương pháp xác định tính chất lý màng 31 2.4.3.7 Phương pháp HPLC 31 2.4.3.8 Phương pháp phun màng 32 2.4.3.9 Định tính hố thực vật 32 2.4.3.10 Đánh giá TAM TYM 32 2.4.3.11 Phương pháp xử lý số liệu 33 CHƯƠNG KẾT QUẢ VÀ THẢO LUẬN 34 3.1 KHẢO SÁT NGUỒN NGUYÊN LIỆU ĐẦU VÀO 34 3.1.1 Khảo sát hình thái, đặc tính nguyên liệu bưởi da xanh 34 3.1.2 Khảo sát quy trình tách chiết cô đặc dịch chiết từ vỏ bưởi 36 3.1.2.1 Ảnh hưởng trình ngâm chiết 36 3.1.2.2 Ảnh hưởng trình chiết có hỗ trợ sóng siêu âm 41 3.1.2.3 Ảnh hưởng q trình chiết có hỗ trợ vi sóng 43 3.1.2.4 Ảnh hưởng q trình chiết hồn lưu Soxhlet 45 download by : skknchat@gmail.com 3.1.2.5 So sánh phương pháp chiết lên hiệu thu hồi thành phần có hoạt tính sinh học dịch chiết 47 3.1.3 Phân tích số thành phần đánh giá hoạt tính kháng khuẩn cao chiết vỏ bưởi 49 3.1.3.1 Định tính hóa thực vật 49 3.1.3.2 Định lượng thành phần cao chiết vỏ bưởi phương pháp HPLC 50 3.1.3.3 Khả kháng khuẩn cao chiết vỏ bưởi 52 3.2 XÂY DỰNG QUY TRÌNH CHẾ TẠO CƠNG THỨC MÀNG PT KẾT HỢP CAO CHIẾT 53 3.2.1 Nghiên cứu công thức màng PT-polysaccharide khác 53 3.2.2 Khảo sát ảnh hưởng tỷ lệ PFE lên tính chất màng chế kết hợp màng PT với số thành phần vỏ bưởi 58 3.3.3 Quy trình cơng nghệ tạo màng PFE/PT hoàn chỉnh 65 3.3 ỨNG DỤNG MÀNG TRONG BẢO QUẢN MÍT THÁI CHẾ BIẾN GIẢM THIỂU 65 CHƯƠNG KẾT LUẬN VÀ KIẾN NGHỊ 73 4.1 KẾT LUẬN 73 4.2 KIẾN NGHỊ 73 TÀI LIỆU THAM KHẢO 74 PHỤ LỤC 89 SẢN PHẨM LIÊN QUAN ĐỀ TÀI 154 download by : skknchat@gmail.com Kinetics of pilot-scale essential oil extraction from pomelo (Citrus maxima) peels Materials and methods 2.1 Materials The material used in the experiment was green skin pomelo (C maxima) harvested in July 2020 from Ben Tre Province in the Mekong Delta in Vietnam (latitudes 10°140 540 ’N and longitudes 106°220 340 ’E) After being selected, fruits are preliminarily treated and peeled off The weight of peels accounted for approximately 15–20% weight of the fruit Before distillation, the albedo was removed from pomelo peel Fresh materials with an average moisture content of 71.13% were used in all experiments Used chemicals are of analytical grade Extraction instrument was a pilot-scale distillation equipment with a volume of 50 L heated by thermal oil The input of each is 5–10 kg The instrument was made of stainless steel (Fig 1a) In addition, the device also has a water-cooled condensation system, a condensing product recovery unit, a temperature sensor and control system, and a stirrer The preliminary processing equipment was a blender (Fig 1b) with an efficiency of 2500 r/min 2.2 Extraction of essential oils Fig Equipment used in the study: (A) Distillation system, (B) Grinder Linear forms of Pseudo first order kinetic models Table Eq Linear form Linear pseudo first order k1 t (4) log ðq1 À qt Þ ¼ log ðq1 Þ À 2:303 (5) ln ðq1 qt ị ẳ lnq1 k1 t Nonlinear pseudo first order (6) qt ¼ q1 ð1 À eÀk1 t Þ (7) qt ¼ q1 ð1 À ð1 À fw Þ:eÀkd1 t Þ (8) qt ¼ q1 ð1 À fw :eÀkw t À ð1 À fw Þ:eÀkd1 t Þ (9) qt ¼ q1 ð1 À fw :eÀkw t À fd1 :eÀkd1 t À fd2 :eÀkd2 t Þ Assumption Plot Parameters Diffusion with no washing [16] log(q1 ,exp – qt) vs t Diffusion with no washing ln(q1 ,exp – qt) vs t q1 = 10intercept k1 = -slopeÂ2.303 q1 = eintercept k1 = -slope Diffusion with no washing Instantaneous washing followed by diffusion [12, 16] Simultaneous washing and unhindered diffusion [16] Simultaneous washing and diffusion [13, 18] qt vs t qt vs t Solver Solver qt vs t Solver qt vs t Solver Linear forms and solutions of Pseudo second order kinetic models Table Eq The pomelo peels, after being removed from albedo, spoilage parts and dirt, were washed and pureed by using the blender Then, kg of pureed pomelo peel was added to the system with 10 kg of water The distillation system operated at 140 °C until the amount of essential oil was exhaustive The estimated time was 180 for fully extracting essential oils from the materi- Linear form Linear pseudo second order t 1 (10) q ẳ k2 q2 ỵ q t t ẳ q1 ỵ k21q2 (11) qt (12) qt ¼ q1 À k21q (13) qt t 1 t 1 qt t ¼ k2 q21 À k2 qe qt Assumption Plot Parameters Two simultaneous (a rapid and a slow extraction) [14, 15, 23] t/qt vs t Two simultaneous (a rapid and a slow extraction) 1/qt vs 1/t Two simultaneous (a rapid and a slow extraction) qt vs qt/t Two simultaneous (a rapid and a slow extraction) qt/t vs qt q1 = slope-1 k2 = slope2ÂinterceptÀ1 q1 = 1/interceptÀ1 k2 = intercept2Âslope-1 q1 = intercept k2 = -(intercept  slope)-1 q1 = - intercept Âslope-1 k2 = slope2 ÂinterceptÀ1 Nonlinear-pseudo-second-order q2 k t Two simultaneous (a rapid and a slow extraction) (14) q ¼ t q1 k2 tỵ1 qt vs t Solver download by : skknchat@gmail.com P.T Dao et al Table Kinetic parameters obtained from linear and nonlinear forms of two models Kinetic model Eq PFO-1 PFO-2 Non PFO-1 Non PFO-2 Non-PFO-3 Non-PFO4 PSO-1 PSO-2 PSO-3 PSO-4 Non-PSO-1 (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) q1;exp (g.kgÀ1) 14.13426 q1;cal (g.kgÀ1) fwÂ102 kwÂ102 (minÀ1) fd1Â102 kd1Â102 (minÀ1) fd2Â102 kd2Â102 (minÀ1) R2 %q 23.6952 23.6944 14.5705 14.5894 14.5701 14.5894 20 20 19.3020 20.5647 18.608 – – – 0.51 233.86 0.09 – – – – – – – – – 2.10 2.07 – – – – – – – – – – 94.98 – – – – – 3.33 3.33 2.09 2.07 2.12 2.07 9.43Â10-2 8.54Â10-2 9.49Â10-4 8.27Â10-4 0.11 – – – – – 3.61 – – – – – – – – – – 2.07 – – – – – 0.91061 0.91063 0.99972 0.99973 0.99968 0.99964 0.99527 0.99901 0.97986 0.97986 0.99608 67.63 67.64 3.09 3.22 3.08 3.22 41.50 41.50 36.56 45.50 31.65 Where k1 (minÀ1) is the rate constant of the model, qt (g) is the amount of essential oils obtained at time t, and q1 (g) is the amount of essential oil at the time of equilibrium The pseudo second order kinetic equation developed by Ho et al [14] is as follows: dqt ẳ k1 q1 qt ị2 qt 3ị Where k2 (kg.g-1minÀ1) is the extraction rate constant The linear and nonlinear forms of the above two equations were evaluated to determine the suitable mechanism and to estimate the kinetic parameters of essential oil extraction from pomelo peel 2.4 Chemical composition of essential oil Fig Experimental data and linear form of first-order kinetic model (Equation (4), Equation (5)) of the extraction process of pomelo oil als [20–22] Distillation time is calculated from the first drop of liquid that appears The resulting mixture consists of essential oil and water and was dehydrated with Na2SO4 to afford pure essential oil The amount of essential oil obtained per the weight of fresh pomelo peel (g essential oil per g of fresh raw materials) is calculated using Equation (1): mEO  100 ð1Þ qð%Þ ¼ mused Where, mEO is the weight of essential oil obtained during the extraction (g), mused is the weight of the used material (kg) 2.3 Kinetics of extraction Two kinetic models, namely pseudo-first-order kinetic equation and pseudo-second-order kinetic equation are often used to describe the state of the extraction of essential oils from plant materials The pseudo first order kinetic equation proposed by Lagergren [17] is denoted as follows: dqt ¼ k1 ðq1 À qt Þ qt ð2Þ Chemical composition of the pomelo fruit oil was determined by GC–MS analysis using GC Agilent 6890 N instrument coupled with HP5-MS capillary column (30 mm  0.25 mm  25 mm) and MS 5973 inert The carrier gas was He The split rate was set at 1:50 The pressure of the head column was 9.3 psi 25 mL of essential oil was added with 1.0 mL n- hexane and dehydrated with Na2SO4 The flow rate of was constant at mL/min Injector temperature is 250 °C and the rate of division is 30 Oven programme for samples: 50 °C kept for min, then increased by °C/min to 80 °C, continued to increase by °C/min to 150 °C, continued to increase by 10 °C/min to 200 °C, increase 20 °C/min to 300 °C hold for Results and discussion 3.1 Kinetics of extraction By integrating the model (2) and (3) for boundary conditions (t = 0, qt = 0, and t = t, q1 = qt), the linear and nonlinear forms of the kinetic model could be obtained, as presented in Tables and Among them, model (4) and (10) have recently gained popularity and are the basis of different linear and nonlinear forms The equation (6–8) have been recently described by Milojevi et al [13] and its kinetic derivation (9), which assumed washing and diffusion of essential oils from the materials, reported by Markovic et al [13,18], hypothesized that diffu- download by : skknchat@gmail.com Kinetics of pilot-scale essential oil extraction from pomelo (Citrus maxima) peels Fig Experimental data and linear form of the second-order kinetic model (Equation (10) - (13)) of the extraction of pomelo oil sion is hindered and unhindered by membranes or other barriers present in the materials All equations in Table 1-2 were estimated with Origin version 9.0 software It should be noted that the value of q1 used to conform to Equations (4), (6) is the experimental value taken from the saturation point of the extraction To evaluate the suitability between the model and experimental values, the coefficient of determination (R2) and the percentage deviation of q1 (% q) were used to quantitatively compare the applicability of each model and it is calculated as follows: Á2 PN À i¼1 q1;exp À q1;cal R ¼ À PN À Á2 i¼1 q1;exp À q1;mean Á2 PN À i¼1 q1;cal q1;mean ẳ PN 15ị PN þ i¼1 q1;cal À q1;exp i¼1 q1;cal À q1;mean q% ¼ q1;exp À q1;cal  100 q1;exp In which, q1, exp (g.kgÀ1): 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.kgÀ1): The amount of essential oil obtained from the model by the software; q1, mean (g.kgÀ1): 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 first-order 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, q1 Both of which could be calculated from the graph qt versus t, as shown in Table Similarly, k2 and q of the lin- download by : skknchat@gmail.com 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/qt 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 process 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 distribution 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 visually indiscernible However, plotting them along with the nonlinear-pseudo-second-order 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, q1 , 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 determine the relationship between experimental data and data from the model However, almost all 11 models achieved very high R2, 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 appropriate 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 difference 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 suitability 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%, respectively 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 certain assumptions, the linearization of the curved kinetic to a download by : skknchat@gmail.com 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 R.T Compound Percent 7.157 8.861 8.945 9.792 10.335 11.716 a-Pinene Sabinene b-Pinene b-Mycrene a-Phellandrene Limonene 0.498 0.127 0.071 1.233 0.692 97.379 straight line might have violated the error variance, thus inflating 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 linearization 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 extraction 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: qt ¼ À ð1 À 0:0051ÞeÀ0:0207t ð17Þ q1 In which, qt is the amount of essential oil obtained in the raw material up to time t (g.kgÀ1), q1 is the amount of essential oil obtained until saturation (g.kgÀ1) 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 spectrometry 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 Dlimonene (97.4%) and b-Mycrene (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 aterpinene (1.8%) and a-pinene (0.5%) [4] However, in essential oil extracted from Chinese pomelo (Citrus maxima) fruit by steam distillation showed a lower limonene content of download by : skknchat@gmail.com P.T Dao et al 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] [2] 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 calculated 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 diffusion 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 D-Limonene accounting for 97.4% of total content Current result confirmed the quality of Vietnamese pomelo essential oils and suggested its commercial possibilities and potentials [3] [4] [5] [6] [7] [8] Funding Tan Phat Dao was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup 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 [9] [10] [11] 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 [12] Acknowledgments [13] Tan Phat Dao was funded by Vingroup Joint Stock Company and supported by the Domestic Master/ PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Vingroup Big Data Institute (VINBIGDATA), code VINIF.2020.ThS.11 [14] References [15] [1] N.P.T Nhan, V.T Thanh, M.H Cang, T.D Lam, N.C Huong, L.T.H Nhan, T.T Truc, Q.T Tran, L.G Bach, Microencapsulation of Lemongrass (Cymbopogon citratus) Essential Oil Via Spray Drying: Effects of Feed Emulsion Parameters, Processes (2020) 40, https://doi.org/10.3390/ pr8010040 T.T.Y Nhi, D.T Phat, N.N Quyen, M.H Cang, T.T Truc, L G Bach, N.V Muoi, Effects of Vacuum Concentration on Color, Polyphenol and Flavonoid Contents and Antioxidant Activity of Pomelo Citrus maxima (Burm f.) 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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 download by : skknchat@gmail.com iCITES 2020 IOP Publishing IOP Conf Series: Materials Science and Engineering 1092 (2021) 012075 doi:10.1088/1757-899X/1092/1/012075 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 download by : skknchat@gmail.com iCITES 2020 IOP Publishing IOP Conf Series: Materials Science and Engineering 1092 (2021) 012075 doi:10.1088/1757-899X/1092/1/012075 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 Level Code Independent factors Units -1 +1 A Concentration temperature ℃ 75 80 85 B Concentration time 105 120 135 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 download by : skknchat@gmail.com iCITES 2020 IOP Publishing IOP Conf Series: Materials Science and Engineering 1092 (2021) 012075 doi:10.1088/1757-899X/1092/1/012075 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 A: Concentration Temperature B: Concentration Time TPC order order (℃) (min) (%) 75 105 72.1342 85 105 50.7863 75 135 57.5436 85 135 41.1324 12 75 120 60.3456 85 120 40.0111 80 105 71.0213 13 80 135 56.1876 80 120 70.8145 10 80 120 64.2367 11 11 80 120 72.1576 12 80 120 63.178 13 10 80 120 62.8376 To determine the importance of each coefficient, the value of F– value and "Prob.> F" (p-value) 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 Sum of Squares df Mean Square F-value p-value Model 1299.94 259.99 13.97 0.0016 significant A-Temperature 562.48 562.48 30.21 0.0009 significant B-Time 254.52 254.52 13.67 0.0077 significant AB 6.09 6.09 0.3273 0.5852 A² 430.05 430.05 23.10 0.0020 significant B² 2.48 2.48 0.1333 0.7259 Residual 130.31 18.62 Lack of Fit 50.22 16.74 0.8361 0.5402 not significant Pure Error 80.09 20.02 Cor Total 1430.25 12 Significant p < 0.05, not significant p > 0.05 download by : skknchat@gmail.com iCITES 2020 IOP Publishing IOP Conf Series: Materials Science and Engineering 1092 (2021) 012075 doi:10.1088/1757-899X/1092/1/012075 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