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Huang Y, Liu H, Lu X, Yao L, Chen J, Pan T. Vis-NIR spectroscopic discriminant analysis of aflatoxin B 1 excessive standard in peanut meal as feedstuff materials. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 317:124394. [PMID: 38723467 DOI: 10.1016/j.saa.2024.124394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/24/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.
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Affiliation(s)
- Yongqi Huang
- Department of Biological Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China
| | - Hao Liu
- Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China
| | - Xizhe Lu
- Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China
| | - Lijun Yao
- Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China
| | - Jiemei Chen
- Department of Biological Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China.
| | - Tao Pan
- Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China.
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Tao F, Yao H, Hruska Z, Rajasekaran K, Qin J, Kim M, Chao K. Raman Hyperspectral Imaging as a Potential Tool for Rapid and Nondestructive Identification of Aflatoxin Contamination in Corn Kernels. J Food Prot 2024; 87:100335. [PMID: 39074611 DOI: 10.1016/j.jfp.2024.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024]
Abstract
The potential of Raman hyperspectral imaging with a 785 nm excitation line laser was examined for the detection of aflatoxin contamination in corn kernels. Nine-hundred kernels were artificially inoculated in the laboratory, with 300 kernels each inoculated with AF13 (aflatoxigenic) fungus, AF36 (nonaflatoxigenic) fungus, and sterile distilled water (control). One-hundred kernels from each treatment were subsequently incubated for 3, 5, and 8 days. The mean spectra of single kernels were extracted from the endosperm side and the embryo area of the germ side, and local Raman peaks were identified based upon the calculated reference spectra of aflatoxin-negative and -positive categories separately. The principal component analysis-linear discriminant analysis models were established using different types of variable inputs including original full spectra, preprocessed full spectra, and identified local peaks over kernel endosperm-side, germ-side, and both sides. The results of the established discriminant models showed that the germ-side spectra performed better than the endosperm-side spectra. Based upon the 20 ppb-threshold, the best mean prediction accuracy of 82.6% was achieved for the aflatoxin-negative category using the original spectra in the combined form of both kernel sides, and the best mean prediction accuracy of 86.7% was obtained for the -positive category using the preprocessed germ-side spectra. Based upon the 100 ppb-threshold, the best mean prediction accuracies of 85.0% and 89.6% were achieved for the aflatoxin-negative and -positive categories separately, using the same type of variable inputs for the 20 ppb-threshold. In terms of overall prediction accuracy, the models established upon the original spectra in the combined form of both kernel sides achieved the best predictive performance, regardless of the threshold. The mean overall prediction accuracies of 81.8% and 84.5% were achieved with the 20 ppb- and 100 ppb-thresholds, respectively.
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Affiliation(s)
- Feifei Tao
- Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Haibo Yao
- USDA-ARS, Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA.
| | - Zuzana Hruska
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS 39762, USA
| | - Kanniah Rajasekaran
- USDA-ARS, Food and Feed Safety Research Unit, Southern Regional Research Center, New Orleans, LA 70124, USA
| | - Jianwei Qin
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Moon Kim
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
| | - Kuanglin Chao
- USDA-ARS, Environmental Microbial and Food Safety Laboratory, Beltsville, MD 20705, USA
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Naeem I, Ismail A, Riaz M, Aziz M, Akram K, Shahzad MA, Ameen M, Ali S, Oliveira CAF. Aflatoxins in the rice production chain: A review on prevalence, detection, and decontamination strategies. Food Res Int 2024; 188:114441. [PMID: 38823858 DOI: 10.1016/j.foodres.2024.114441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 04/01/2024] [Accepted: 04/27/2024] [Indexed: 06/03/2024]
Abstract
Rice (Oryza sativa L.) is one of the most consumed cereals that along with several important nutritional constituents typically provide more than 21% of the caloric requirements of human beings. Aflatoxins (AFs) are toxic secondary metabolites of several Aspergillus species that are prevalent in cereals, including rice. This review provides a comprehensive overview on production factors, prevalence, regulations, detection methods, and decontamination strategies for AFs in the rice production chain. The prevalence of AFs in rice is more prominent in African and Asian than in European countries. Developed nations have more stringent regulations for AFs in rice than in the developing world. The contamination level of AFs in the rice varied at different stages of rice production chain and is affected by production practices, environmental conditions comprising temperature, humidity, moisture, and water activity as well as milling operations such as de-husking, parboiling, and polishing. A range of methods including chromatographic techniques, immunochemical methods, and spectrophotometric methods have been developed, and used for monitoring AFs in rice. Chromatographic methods are the most used methods of AFs detection followed by immunochemical techniques. AFs decontamination strategies adopted worldwide involve various physical, chemical, and biological strategies, and even using plant materials. In conclusion, adopting good agricultural practices, implementing efficient AFs detection methods, and developing innovative aflatoxin decontamination strategies are imperative to ensure the safety and quality of rice for consumers.
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Affiliation(s)
- Iqra Naeem
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Amir Ismail
- Department of Food Safety and Quality Management, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan.
| | - Muhammad Riaz
- Department of Food Safety and Quality Management, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Mubashir Aziz
- Department of Microbiology and Molecular Genetics, Bahauddin Zakariya University, Multan, Pakistan
| | - Kashif Akram
- Department of Food Science, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan
| | - Muhammad A Shahzad
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Mavra Ameen
- Department of Food Science & Technology, Faculty of Food Science & Nutrition, Bahauddin Zakariya University, Multan, Pakistan
| | - Sher Ali
- Department of Food Engineering, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil
| | - Carlos A F Oliveira
- Department of Food Engineering, Faculty of Animal Science and Food Engineering, University of São Paulo, Pirassununga, São Paulo, Brazil.
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He X, You J, Yang X, Li L, Shen F, Wang L, Li P, Fang Y. Quantitative prediction of AFB 1 in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 310:123900. [PMID: 38262292 DOI: 10.1016/j.saa.2024.123900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/30/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024]
Abstract
This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B1 (AFB1) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (μa) and reduced scattering (μ's) coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB1 contents in six different kinds of edible oils. A research process of "Monte Carlo (MC) simulation - phantom verification - actual sample validation" was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the μa, μ's at emission wavelength (μa,em, μ's,em) and μa at excitation wavelength (μa,ex), however increased with the μ's at excitation wavelength (μ's,ex). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (μeff,ex, μeff,em) and μ's,ex. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB1 contamination. The LSSVR model for AFB1 based on μeff,ex, μeff,em, μ's,ex and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (Rc) and the validation (Rv) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB1 content in different types of vegetable oils.
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Affiliation(s)
- Xueming He
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China.
| | - Jie You
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Xiaoyun Yang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Longwen Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Fei Shen
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Liu Wang
- Key Iaboratory of Traceability for Agricultural Genetically Modified Organisms, Ministry of Agriculture and Rural Affairs, Hangzhou 310022, China
| | - Peng Li
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
| | - Yong Fang
- College of Food Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China; Collaborative Innovation Center for Modern Grain Circulation and Safety/Key Laboratory of Grains and Oils Quality Control and Processing, Nanjing 210023, China
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Shi S, Tang Z, Ma Y, Cao C, Jiang Y. Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice. Crit Rev Food Sci Nutr 2023:1-23. [PMID: 38010116 DOI: 10.1080/10408398.2023.2284246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.
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Affiliation(s)
- Shijie Shi
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Zihan Tang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yingying Ma
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Cougui Cao
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Yang Jiang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
- Shuangshui Shuanglü Institute, Huazhong Agricultural University, Wuhan, Hubei, China
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Rapid detection of fumonisin B1 and B2 in ground corn samples using smartphone-controlled portable near-infrared spectrometry and chemometrics. Food Chem 2022; 384:132487. [DOI: 10.1016/j.foodchem.2022.132487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
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Determination of aflatoxin B1 value in corn based on Fourier transform near-infrared spectroscopy: Comparison of optimization effect of characteristic wavelengths. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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8
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Green and sustainable technologies for the decontamination of fungi and mycotoxins in rice: A review. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.04.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Ong P, Tung IC, Chiu CF, Tsai IL, Shih HC, Chen S, Chuang YK. Determination of aflatoxin B1 level in rice (Oryza sativa L.) through near-infrared spectroscopy and an improved simulated annealing variable selection method. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.108886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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10
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Nutritional Changes and Early Warning of Moldy Rice under Different Relative Humidity and Storage Temperature. Foods 2022; 11:foods11020185. [PMID: 35053916 PMCID: PMC8775272 DOI: 10.3390/foods11020185] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 12/25/2021] [Accepted: 01/05/2022] [Indexed: 02/04/2023] Open
Abstract
Processed unhusked rice is prone to mildew during storage. In this study, the storage conditions were simulated at temperatures of 20, 30, and 35 °C and a relative humidity of 40%, 60% and 80%, respectively. The water, fatty acid, and total starch content and the peak viscosity, mold colony number, protein secondary structure, and spatial structure of rice were monitored in order to propose the critical point of mildew during storage. In the process of rice from lively to moldy, the water content, fatty acid contents and the peak viscosity were increased. The total starch content decreased and then showed a slow increasing trend, while the microstructure of the powder particles changed from smooth and complete to loosen and hollow. With the increase in storage time, the vibration of the amide Ⅰ band of the rice samples decreased slightly, indicating that the total contents of β-fold, β-turn, α-helix, and random curl of the rice protein also changed. PCA (Principal Component Analysis) analysis showed that rice mildew index was closely related to temperature and humidity during storage. In our investigation, the best and most suitable temperature and relative humidity for rice storge is 20 °C and 40%, respectively. These results suggested that temperature and environmental humidity are vital factors affecting the physicochemical properties and nutrient changes, which provides a theoretical basis for the early warning of rice mildew during storage.
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Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Research advancements in optical imaging and spectroscopic techniques for nondestructive detection of mold infection and mycotoxins in cereal grains and nuts. Compr Rev Food Sci Food Saf 2021; 20:4612-4651. [PMID: 34338431 DOI: 10.1111/1541-4337.12801] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/07/2021] [Accepted: 06/15/2021] [Indexed: 12/01/2022]
Abstract
Cereal grains and nuts are represented as the economic backbone of many developed and developing countries. Kernels of cereal grains and nuts are prone to mold infection under high relative humidity and suitable temperature conditions in the field as well as storage conditions. Health risks caused by molds and their molecular metabolite mycotoxins are, therefore, important topics to investigate. Strict regulations have been developed by international trade regulatory bodies for the detection of mold growth and mycotoxin contamination across the food chain starting from the harvest to storage and consumption. Molds and aflatoxins are not evenly distributed over the bulk of grains, thus appropriate sampling for detection and quantification is crucial. Existing reference methods for mold and mycotoxin detection are destructive in nature as well as involve skilled labor and hazardous chemicals. Also, these methods cannot be used for inline sorting of the infected kernels. Thus, analytical methods have been extensively researched to develop the one that is more practical to be used in commercial detection and sorting processes. Among various analytical techniques, optical imaging and spectroscopic techniques are attracting growers' attention for their potential of nondestructive and rapid inline identification and quantification of molds and mycotoxins in various food products. This review summarizes the recent application of rapid and nondestructive optical imaging and spectroscopic techniques, including digital color imaging, X-ray imaging, near-infrared spectroscopy, fluorescent, multispectral, and hyperspectral imaging. Advance chemometric techniques to identify very low-level mold growth and mycotoxin contamination are also discussed. Benefits, limitations, and challenges of deploying these techniques in practice are also presented in this paper.
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Affiliation(s)
- Gayatri Mishra
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Brajesh Kumar Panda
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Wilmer Ariza Ramirez
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Hyewon Jung
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Chandra B Singh
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia.,Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, Lethbridge, Alberta, Canada
| | - Sang-Heon Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
| | - Ivan Lee
- UniSA STEM, University of South Australia, Mawson Lakes, South Australia, Australia
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Pandiselvam R, Sruthi NU, Kumar A, Kothakota A, Thirumdas R, Ramesh S, Cozzolino D. Recent Applications of Vibrational Spectroscopic Techniques in the Grain Industry. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1904253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- R. Pandiselvam
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - N. U. Sruthi
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Ankit Kumar
- Agricultural and Food Engineering Department, Indian Institute of Technology (IIT), Kharagpur, India
| | - Anjineyulu Kothakota
- Agro-Processing & Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Trivandrum, India
| | - Rohit Thirumdas
- Department of Food Process Technology, College of Food Science & Technology, Telangana, India
| | - S.V. Ramesh
- Physiology,Biochemistry and Post Harvest Technology Division, ICAR –Central Plantation Crops Research Institute, Kasaragod, India
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), the University of Queensland, Brisbane, Australia
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