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Wang Y, Xing L, He HJ, Zhang J, Chew KW, Ou X. NIR sensors combined with chemometric algorithms in intelligent quality evaluation of sweetpotato roots from 'Farm' to 'Table': Progresses, challenges, trends, and prospects. Food Chem X 2024; 22:101449. [PMID: 38784692 PMCID: PMC11112285 DOI: 10.1016/j.fochx.2024.101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/26/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
NIR sensors, in conjunction with advanced chemometric algorithms, have proven to be a powerful and efficient tool for intelligent quality evaluation of sweetpotato roots throughout the entire supply chain. By leveraging NIR data in different wavelength ranges, the physicochemical, nutritional and antioxidant compositions, as well as variety classification of sweetpotato roots during the different stages were adequately evaluated, and all findings involving quantitative and qualitative investigations from the beginning to the present were summarized and analyzed comprehensively. All chemometric algorithms including both linear and nonlinear employed in NIR analysis of sweetpotato roots were introduced in detail and their calibration performances in terms of regression and classification were assessed and discussed. The challenges and limitations of current NIR application in quality evaluation of sweetpotato roots are emphasized. The prospects and trends covering the ongoing advancements in software and hardware are suggested to support the sustainable and efficient sweetpotato processing and utilization.
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Affiliation(s)
- Yuling Wang
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Longzhu Xing
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Jie Zhang
- Henan Xinlianxin Chemical Industry Co., Ltd., Xinxiang 453003, China
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Xingqi Ou
- School of Agriculture, Henan Institute of Science and Technology, Xinxiang 453003, China
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He HJ, Wang Y, Wang Y, Al-Maqtari QA, Liu H, Zhang M, Ou X. Towards rapidly quantifying and visualizing starch content of sweet potato [Ipomoea batatas (L.) Lam] based on NIR spectral and image data fusion. Int J Biol Macromol 2023; 242:124748. [PMID: 37164142 DOI: 10.1016/j.ijbiomac.2023.124748] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 04/13/2023] [Accepted: 05/01/2023] [Indexed: 05/12/2023]
Abstract
This study aimed to achieve the rapid quantification and visualization of the starch content in sweet potato via near-infrared (NIR) spectral and image data fusion. The hyperspectral images of the sweet potato samples containing 900-1700 nm spectral information within every pixel were collected. The spectra were preprocessed, analyzed and the 18 informative wavelengths were finally extracted to relate to the measured starch content using the multiple linear regression (MLR) algorithm, producing a good quantitative prediction accuracy with a correlation coefficient of prediction (rP) of 0.970 and a root-mean-square error of prediction (RMSEP) of 0.874 g/100 g by an external validation using a set of dependent samples. The MLR model was further verified in terms of soundness and predictive validity via F-test and t-test, and then transferred to each pixel of the original two dimensional images with the help of a developed algorithm, generating color distribution maps to achieve the vivid visualization of the starch distribution. The study demonstrated that the fusion of the NIR spectral and image data provided a good strategy for the rapidly and nondestructively monitoring the starch content of sweet potato. This technique can be applied to industrial use in the future.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Yangyang Wang
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Qais Ali Al-Maqtari
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
| | - Mian Zhang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China.
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3
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Ji J, He Y, Zhao K, Zhang M, Li M, Li H. Quality Information Detection of Agaricus bisporus Based on a Portable Spectrum Acquisition Device. Foods 2023; 12:2562. [PMID: 37444303 DOI: 10.3390/foods12132562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023] Open
Abstract
As one of the most popular edible fungi in the market, the quality of Agaricus bisporus will determine its sales volume. Therefore, to achieve rapid and nondestructive testing of the quality of Agaricus bisporus, this study first built a portable spectrum acquisition device for Agaricus bisporus. The Ocean Spectromeper was used to calibrate the spectral data of the device, and the linear regression analysis method was combined to analyze the two. The results showed that the Pearson correlation coefficient of significance between the two was 0.98. Then, the spectral data of Agaricus bisporus were collected, the spectral characteristic wavelength of Agaricus bisporus was extracted by the SPA and PCA algorithms, and the moisture content and whiteness prediction models based on a BP neural network and PLSR, respectively, were built. The parameters of the BP neural network model were optimized by SSA. The R2 values for the final moisture content and the predicted whiteness were 0.95 and 0.99, and the RMSE values were 5.04% and 0.60, respectively. The results show that the portable spectral acquisition and analysis device can be used for the accurate and rapid quality detection of Agaricus bisporus.
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Affiliation(s)
- Jiangtao Ji
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
- Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China
| | - Yongkang He
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Kaixuan Zhao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
- Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengke Zhang
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Mengsong Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
| | - Hongzhen Li
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
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He HJ, Wang Y, Wang Y, Liu H, Zhang M, Ou X. Simultaneous quantifying and visualizing moisture, ash and protein distribution in sweet potato [ Ipomoea batatas (L.) Lam] by NIR hyperspectral imaging. Food Chem X 2023; 18:100631. [PMID: 36926310 PMCID: PMC10010985 DOI: 10.1016/j.fochx.2023.100631] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2023] [Accepted: 03/05/2023] [Indexed: 03/10/2023] Open
Abstract
This study aimed to achieve the rapid evaluation of moisture, ash and protein of sweet potato simultaneously by near-infrared (NIR) hyperspectral imaging (900-1700 nm). Hyperspectral images of 300 samples for each parameter were acquired and the spectra within images were extracted, averaged and preprocessed to relate to the three measured parameters, using partial least squares (PLS) algorithm, respectively, resulting in good performances. Nine, eleven and eleven informative wavelengths were selected to accelerate the prediction of the three parameters, generating a correlation coefficient of prediction (r P) of 0.984, 0.905, 0.935 and root mean square error of prediction (RMSEP) of 0.907%, 0.138%, 0.0941% for moisture, ash and protein, respectively. By transferring the best optimized PLS models to generate color chemical maps, the distributions and variations of the three parameters were visualized. NIR hyperspectral imaging is promising and can be applied to simultaneously evaluate multiple quality parameters of sweet potato.
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Affiliation(s)
- Hong-Ju He
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China.,School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637459, Singapore
| | - Yuling Wang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Yangyang Wang
- School of Food Science, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Hongjie Liu
- School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China
| | - Mian Zhang
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
| | - Xingqi Ou
- School of Life Science & Technology, Henan Institute of Science and Technology, Xinxiang 453003, China
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Xu Y, Wu W, Chen Y, Zhang T, Tu K, Hao Y, Cao H, Dong X, Sun Q. Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds. FRONTIERS IN PLANT SCIENCE 2022; 13:1031849. [PMID: 36523615 PMCID: PMC9745075 DOI: 10.3389/fpls.2022.1031849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/08/2022] [Indexed: 06/17/2023]
Abstract
The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.
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Affiliation(s)
- Yanan Xu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Weifeng Wu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yi Chen
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Keling Tu
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Yun Hao
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Hailu Cao
- Hengde Materia Medica (Beijing) Agricultural Technology Co., Ltd., Beijing, China
| | - Xuehui Dong
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
| | - Qun Sun
- College of Agronomy and Biotechnology, Department of Plant Genetics & Breeding and Seed Science/Chinese Medicinal Herbs Research Center, China Agricultural University/The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research, Ministry of Agriculture and Rural Affairs/Beijing Key Laboratory of Crop Genetic Improvement, Beijing, China
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6
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Rashid MT, Liu K, Jatoi MA, Safdar B, Lv D, Li Q. Energy efficient drying technologies for sweet potatoes: Operating and drying mechanism, quality-related attributes. Front Nutr 2022; 9:1040314. [PMID: 36337660 PMCID: PMC9631448 DOI: 10.3389/fnut.2022.1040314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
Sweet potatoes (SPs) are a versatile tuberous crop used as subsistence and cash crop in raw and processed forms. The major issue with SPs is post-harvest losses, which result in noticeable quality decline because of inappropriate handling, storage, delayed transit, and sales, as well as microbiological and enzymatic activity. Drying is an excellent strategy for managing short postharvest storage life, preserving nutrients, and maximizing long-term benefits. However, several parameters must be considered before drying SPs, such as relative humidity, temperature, drying duration, size, and shape. The current review looks at the factors influencing SPs' moisture loss, drying kinetics, diverse drying methods, pretreatments, operating conditions, and their efficacy in improving the drying process, functional, and nutritional qualities. An optimal drying process is required to preserve SPs to obtain concentrated nutrients and improve energy efficiency to be ecofriendly. Drying sweet potatoes using traditional methods such as sun or open-air drying was found to be a slow process that could result in a lower quality. Various advanced drying techniques, like vacuum, infrared, freeze drying, and pretreatments such as ultrasound and osmotic dehydration, have been developed and are successfully used globally. The best-fit thin-layer models (Hii, Page, two-term, logarithmic) utilized for drying SPs and appropriate modeling methods for optimizing drying procedures are also discussed.
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Affiliation(s)
- Muhammad Tayyab Rashid
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
| | - Kunlun Liu
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
- *Correspondence: Kunlun Liu
| | | | - Bushra Safdar
- Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University (BTBU), Beijing, China
| | - Dingyang Lv
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
| | - Qingyun Li
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou, China
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Zhang H, Hou Q, Luo B, Tu K, Zhao C, Sun Q. Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology. FRONTIERS IN PLANT SCIENCE 2022; 13:1015891. [PMID: 36247557 PMCID: PMC9554440 DOI: 10.3389/fpls.2022.1015891] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected via hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future.
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Affiliation(s)
- Han Zhang
- Department of Seed Science & Biotechnology, The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research Ministry of Agriculture and Rural Affairs (MOA), Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qiling Hou
- Institute of Hybrid Wheat, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Keling Tu
- Department of Seed Science & Biotechnology, The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research Ministry of Agriculture and Rural Affairs (MOA), Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Changping Zhao
- Institute of Hybrid Wheat, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qun Sun
- Department of Seed Science & Biotechnology, The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research Ministry of Agriculture and Rural Affairs (MOA), Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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8
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Leite CEC, Souza BDKF, Manfio CE, Wamser GH, Alves DP, de Francisco A. Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:852709. [PMID: 35599896 PMCID: PMC9119308 DOI: 10.3389/fpls.2022.852709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/07/2022] [Indexed: 06/15/2023]
Abstract
A sample set of 18 sweet potatoes [Ipomoea batatas (L.) Lam] segmented into six registered cultivars and 12 new varieties were evaluated. The 142 tuberous roots were obtained from a sweet potato germplasm bank (BAG-sweet potato; -27.417713768824555 and -49.64874168439556), specifically from plants belonging to a sweet potato breeding program. All samples were characterized according to their morphology, instrumental pulp color, proximate composition, and total dietary fiber. The analytical results were submitted to parametric and non-parametric statistical tests for sample variance data comparison. Moreover, the screening of the cultivars and new varieties was performed by exploratory statistical analysis, factor analysis (FA), and principal component analysis (PCA). From the sixteen independent variables that characterized the samples, the exploratory FA identified thirteen that had a communality greater than 0.7, with 92.08% of assertiveness. The PCA generated 4 principal components able to account for 84.01% of the explanatory variance. So, among the six registered cultivars, SCS372 Marina and SCS370 Luiza showed the capability to be employed as cultivars for production. Among the 12 sweet potato new varieties, samples 17025-13, 17125-10, and 17117 met the requirements for patent and registration. These results will be useful to farmers who wish to use these sweet potatoes in the development of their crops.
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Affiliation(s)
- Cláudio Eduardo Cartabiano Leite
- Cereal Science and Technology Laboratory, Food Science Post-Graduation Program (PPGCAL), Agrarian Sciences Center, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil
| | - Brunna de Kácia Ferreira Souza
- Cereal Science and Technology Laboratory, Food Science and Technology Department (CTA), Agrarian Sciences Center, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil
| | - Candida Elisa Manfio
- Agricultural Research and Rural Extension of Santa Catarina (EPAGRI) – Ituporanga Experimental Station, Ituporanga, Brazil
| | - Gerson Henrique Wamser
- Agricultural Research and Rural Extension of Santa Catarina (EPAGRI) – Ituporanga Experimental Station, Ituporanga, Brazil
| | - Daniel Pedrosa Alves
- Agricultural Research and Rural Extension of Santa Catarina (EPAGRI) – Ituporanga Experimental Station, Ituporanga, Brazil
| | - Alicia de Francisco
- Cereal Science and Technology Laboratory, Food Science Post-Graduation Program (PPGCAL), Agrarian Sciences Center, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil
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9
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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10
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Su WH, Xue H. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods 2021; 10:2146. [PMID: 34574253 PMCID: PMC8472741 DOI: 10.3390/foods10092146] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 11/16/2022] Open
Abstract
Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology.
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Affiliation(s)
- Wen-Hao Su
- Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing 100083, China;
| | - Huidan Xue
- School of Economics and Management, Beijing University of Technology, Beijing 100124, China
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11
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Ren Y, Lin X, Lei T, Sun DW. Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit Rev Food Sci Nutr 2021; 62:4267-4293. [PMID: 34275402 DOI: 10.1080/10408398.2021.1947773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
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12
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Peng J, Wang K, Ma C, Long J, Tu K, Pan L. Determination of anthocyanin and moisture content of purple sweet potatoes during drying process by their optical properties in the 400-1050 nm range. Food Chem 2021; 359:129811. [PMID: 33951612 DOI: 10.1016/j.foodchem.2021.129811] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 03/27/2021] [Accepted: 03/29/2021] [Indexed: 10/21/2022]
Abstract
This research investigated the potential of using optical properties to assess quality attributes of purple sweet potatoes (PSPs) during drying process. The absorption (μa) and scattering (μs') properties of PSPs during drying were determined at 400-1050 nm using a single integrating sphere system. The absorption spectra were marked by pronounced anthocyanin peaks around 545 nm, and very small water peaks around 980 nm. The average μs' was relatively flat in the 670-1000 nm range, which increased as drying time increased. Good prediction of anthocyanins (determined by pH-differential method) and moisture (determined by oven-drying method) contained in PSPs were obtained using their optical properties through SVM models, with determination coefficients of prediction (R2p) of 0.866-0.917, and residual predictive deviation (RPD) of 2.626-3.190. These results were consistent with correlation results between optical properties and the two quality parameters, suggesting the former can be used for predicting PSP quality during drying.
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Affiliation(s)
- Jing Peng
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Ke Wang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Chen Ma
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Jiamei Long
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China.
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13
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Tian X, Aheto JH, Bai J, Dai C, Ren Y, Chang X. Quantitative analysis and visualization of moisture and anthocyanins content in purple sweet potato by Vis–NIR hyperspectral imaging. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.15128] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xiao‐Yu Tian
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Joshua H. Aheto
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Jun‐Wen Bai
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
| | - Chunxia Dai
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of Electrical and Information Engineering Jiangsu University Zhenjiang P.R. China
| | - Yi Ren
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
- School of Smart Agriculture Suzhou Polytechnic Institute of Agriculture Suzhou P.R. China
| | - Xianhui Chang
- School of Food and Biological Engineering Jiangsu University Zhenjiang P.R. China
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14
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Shi X, Yang Y, Li Z, Wang X, Liu Y. Moisture transfer and microstructure change of banana slices during contact ultrasound strengthened far-infrared radiation drying. INNOV FOOD SCI EMERG 2020. [DOI: 10.1016/j.ifset.2020.102537] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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15
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Zhang T, Fan S, Xiang Y, Zhang S, Wang J, Sun Q. Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 239:118488. [PMID: 32470809 DOI: 10.1016/j.saa.2020.118488] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 04/26/2020] [Accepted: 05/13/2020] [Indexed: 05/10/2023]
Abstract
Two hyperspectral imaging (HSI) systems, visible/near infrared (Vis/NIR, 304-1082 nm) and short wave infrared (SWIR, 930-2548 nm), were used for the first time to comprehensively predict the changes in quality of wheat seeds based on three vigour parameters: germination percentage (GP, reflecting the number of germinated seedling), germination energy (GE, reflecting the speed and uniformity of seedling emergence), and simple vigour index (SVI, reflecting germination percentage and seedling weight). Each sample contained a small number of wheat seeds, which were obtained by high temperature and humidity-accelerated aging (0, 2, and 3 days) to simulate storage. The spectra of these samples were collected using HSI systems. After collection, each seed sample underwent a standard germination test to determine their GP, GE, and SVI. Then, several pretreatment methods and the partial least-squares regression algorithm (PLS-R) were used to establish quantitative models. The models for the Vis/NIR region obtained excellent performance, and most effective wavelengths (EWs) were selected in the Vis/NIR region by the successive projections algorithm (SPA) and regression coefficients (RC). Subsequently, PLS-R-RC models using selected wavebands (sixteen wavebands for GP, 14 wavebands for GE, and 16 wavebands for SVI) exhibited similar performance to the PLS-R models based on the full wavebands. The best R2 results obtained in the simplified models' prediction sets were 0.921, 0.907, and 0.886, with RMSE values of 4.113%, 5.137%, and 0.024, for GP, GE, and SVI, respectively. Distribution maps of GP, GE, and SVI were produced by applying these simplified PLS models. By interpreting the EWs and building prediction models, soluble protein and sugar content were demonstrated to have a relationship with spectral information. In summary, the present results lay a foundation towards the development of a significantly simpler, more comprehensive, and non-destructive hyperspectral-based sorting system for determining the vigour of wheat seeds.
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Affiliation(s)
- Tingting Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
| | - Yingying Xiang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Shujie Zhang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Jianhua Wang
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
| | - Qun Sun
- Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China.
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16
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Feng L, Cheng L, Zhang B, Jiang B, Dong Z. Estimating moisture content and physical properties of sweet potato based on the multiparameter of electrical impedance spectroscopy. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14775] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Longlong Feng
- College of Engineering Shenyang Agricultural University Shenyang China
| | - Lixin Cheng
- College of Engineering Shenyang Agricultural University Shenyang China
| | - Benhua Zhang
- College of Engineering Shenyang Agricultural University Shenyang China
| | - Binzhang Jiang
- College of Engineering Shenyang Agricultural University Shenyang China
| | - Zhanwei Dong
- College of Engineering Shenyang Agricultural University Shenyang China
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17
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Zhang J, Liu Y, Sun Z, Bai X, Chen Y. Drying characteristics and optimization of ultrasound‐strengthened cold air drying combined with sequential far‐infrared radiation drying on potato. J FOOD PROCESS PRES 2020. [DOI: 10.1111/jfpp.14632] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jiayi Zhang
- College of Food & Bioengineering Henan University of Science and Technology Luoyang China
| | - Yunhong Liu
- College of Food & Bioengineering Henan University of Science and Technology Luoyang China
| | - Zhenke Sun
- College of Food & Bioengineering Henan University of Science and Technology Luoyang China
| | - Xiting Bai
- College of Food & Bioengineering Henan University of Science and Technology Luoyang China
| | - Yunfei Chen
- College of Food & Bioengineering Henan University of Science and Technology Luoyang China
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18
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19
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Weng S, Yu S, Dong R, Pan F, Liang D. Nondestructive detection of storage time of strawberries using visible/near-infrared hyperspectral imaging. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2020. [DOI: 10.1080/10942912.2020.1716793] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Ronglu Dong
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Fangfang Pan
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| | - Dong Liang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
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20
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Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis. Foods 2020; 9:foods9010094. [PMID: 31963170 PMCID: PMC7022740 DOI: 10.3390/foods9010094] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 01/10/2020] [Accepted: 01/14/2020] [Indexed: 01/09/2023] Open
Abstract
Color index and water content are important indicators for evaluating the quality of fresh-cut potato tuber slices. In this study, hyperspectral imaging combined with multivariate analysis was used to detect the color parameters (L*, a*, b*, Browning index (BI), L*/b*) and water content of fresh-cut potato tuber slices. The successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic wavelengths, partial least squares (PLS) and least squares support vector machine (LS-SVM) were utilized to establish regression models. For color prediction, R2c, R2p and RPD of all the LSSVM models established for the five color indicators L*, a*, b*, BI, L*/b* were exceeding 0.90, 0.84 and 2.1, respectively. For water content prediction, R2c, R2p, and RPD of the LSSVM models were over 0.80, 0.77 and 1.9, respectively. LS-SVM model based on full spectra was used to reappear the spatial distribution of color and water content in fresh-cut potato tuber slices by pseudo-color imaging since it performed best in most cases. The results illustrated that hyperspectral imaging could be an effective method for color and water content prediction, which could provide solid theoretical basis for subsequent grading and processing of fresh-cut potato tuber slices.
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21
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Effect of Ultrasonic Power on Water Removal Kinetics and Moisture Migration of Kiwifruit Slices During Contact Ultrasound Intensified Heat Pump Drying. FOOD BIOPROCESS TECH 2020. [DOI: 10.1007/s11947-019-02401-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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22
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Liu Y, Zeng Y, Hu R, Sun X. Effect of contact ultrasonic power on moisture migration during far‐infrared radiation drying of kiwifruit. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13235] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yunhong Liu
- College of Food & BioengineeringHenan University of Science and Technology Luoyang China
| | - Ya Zeng
- College of Food & BioengineeringHenan University of Science and Technology Luoyang China
| | - Runrun Hu
- College of Food & BioengineeringHenan University of Science and Technology Luoyang China
| | - Xue Sun
- College of Food & BioengineeringHenan University of Science and Technology Luoyang China
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23
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Liu Y, Wang Q, Gao X, Xie A. Total phenolic content prediction in
Flos Lonicerae
using hyperspectral imaging combined with wavelengths selection methods. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13224] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yunhong Liu
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Qingqing Wang
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Xiuwei Gao
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Anguo Xie
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
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24
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Yoplac I, Avila-George H, Vargas L, Robert P, Castro W. Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools. Heliyon 2019; 5:e02122. [PMID: 31388576 PMCID: PMC6675954 DOI: 10.1016/j.heliyon.2019.e02122] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 07/07/2019] [Accepted: 07/18/2019] [Indexed: 12/25/2022] Open
Abstract
This work evaluates near-infrared (NIR) spectroscopy coupled with chemometric tools for determining the superficial content of citral (SCCt) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios (CDR) and inlet air temperatures (IAT). From each treatment, six samples were extracted, and their SCCt and NIR absorption spectral profiles were measured. Then, the spectral profiles, pretreated and randomly divided into modeling and validation datasets, were used to build the following prediction models: principal component analysis-multilinear regression (PCA-MLR), principal component analysis-artificial neural network (PCA-ANN), partial least squares regression (PLSR) and an artificial neural network (ANN). During the validation stage, the models showed R2 values from 0.73 to 0.96 and a root mean squared error (RMSE) range of [0.061–0.140]. Moreover, when the models were compared, the full and optimized ANN models showed the best fits. According to this study, NIR coupled with chemometric tools has the potential for application in determining SCCt on microparticles, particularly when using ANN models.
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Affiliation(s)
- Ives Yoplac
- Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru
- Escuela de Posgrado, Programa Doctoral en Ciencia de Alimentos, Universidad Nacional Agraria La Molina, Lima, Peru
- Corresponding author at: Facultad de Ingeniería Zootecnista, Agronegocios y Biotecnología, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, Peru.
| | - Himer Avila-George
- Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca, Jalisco 46600, Mexico
| | - Luis Vargas
- Facultad de Industrias Alimentarias, Universidad Nacional Agraria La Molina, Lima, Peru
| | - Paz Robert
- Departamento de Ciencia de Alimentos y Tecnología Química, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago, Chile
| | - Wilson Castro
- Facultad de Ingeniería, Universidad Privada del Norte, Cajamarca 06002, Peru
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25
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Fu DD, Wang QH, Ma MH, Ma YX, Vong CN. Prediction and visualisation of S-ovalbumin content in egg whites using hyperspectral images. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2019. [DOI: 10.1080/10942912.2019.1628775] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Dan Dan Fu
- College of Engineering, Huazhong Agricultural University, Wuhan, China
- National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan, China
| | - Qiao Hua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan, China
- National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan, China
- Ministry of Agriculture Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Huazhong Agricultural University, Wuhan, China
| | - Mei Hu Ma
- National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan, China
- College of Food Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Yi Xiao Ma
- College of Engineering, Huazhong Agricultural University, Wuhan, China
- National Research and Development Center for Egg Processing, Huazhong Agricultural University, Wuhan, China
| | - Chin Nee Vong
- Division of Food Systems & Bioengineering, College of Agriculture, Food & Natural Resources, University of Missouri, Columbia, MO, USA
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26
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Wang Q, Liu Y, Gao X, Xie A, Yu H. Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00180-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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27
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Suitability of LF-NMR to analysis water state and predict dielectric properties of Chinese yam during microwave vacuum drying. Lebensm Wiss Technol 2019. [DOI: 10.1016/j.lwt.2019.02.017] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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28
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P V, Dash SK, Rayaguru K. Post-Harvest Processing and Utilization of Sweet Potato: A Review. FOOD REVIEWS INTERNATIONAL 2019. [DOI: 10.1080/87559129.2019.1600540] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Vithu P
- Department of Agricultural Processing and Food Engineering, College of Agricultural Engineering and Technology, OUAT, Bhubaneswar, India
| | - Sanjaya K Dash
- College of Agricultural Engineering and Technology, OUAT, Bhubaneswar, India
| | - Kalpana Rayaguru
- Department of Agricultural Processing and Food Engineering, College of Agricultural Engineering and Technology, OUAT, Bhubaneswar, India
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29
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Ma J, Sun DW, Pu H, Cheng JH, Wei Q. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications. Annu Rev Food Sci Technol 2019; 10:197-220. [DOI: 10.1146/annurev-food-032818-121155] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Hyperspectral imaging (HSI) is a technology integrating optical sensing technologies of imaging, spectroscopy, and chemometrics. The sensor of HSI can obtain both spatial and spectral information simultaneously. Therefore, the chemical and physical information of food products can be monitored in a rapid, nondestructive, and noncontact manner. There are numerous reports and papers and much research dealing with the applications of HSI in food in recent years. This review introduces the principle of HSI technology, summarizes its recent applications in food, and pinpoints future trends.
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Affiliation(s)
- Ji Ma
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland;,
| | - Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Jun-Hu Cheng
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China
- Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
- Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
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30
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Su WH, Sun DW. Advanced Analysis of Roots and Tubers by Hyperspectral Techniques. ADVANCES IN FOOD AND NUTRITION RESEARCH 2018; 87:255-303. [PMID: 30678816 DOI: 10.1016/bs.afnr.2018.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
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Affiliation(s)
- Wen-Hao Su
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerised Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Dublin, Ireland.
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31
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Feasibility of using spectral profiles for modeling water activity in five varieties of white quinoa grains. J FOOD ENG 2018. [DOI: 10.1016/j.jfoodeng.2018.06.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9834-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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33
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Liu Y, Sun Y, Xie A, Yu H, Yin Y, Li X, Duan X. Potential of Hyperspectral Imaging for Rapid Prediction of Anthocyanin Content of Purple-Fleshed Sweet Potato Slices During Drying Process. FOOD ANAL METHOD 2017. [DOI: 10.1007/s12161-017-0950-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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