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van Wyngaard E, Blancquaert E, Nieuwoudt H, Aleixandre-Tudo JL. Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification. APPLIED SPECTROSCOPY 2024; 78:523-537. [PMID: 38403903 DOI: 10.1177/00037028241232004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.
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Affiliation(s)
- Elizma van Wyngaard
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Erna Blancquaert
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Hélène Nieuwoudt
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Jose Luis Aleixandre-Tudo
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
- Instituto de Ingeniería de Alimentos-Food UPV, Departamento de Tecnología de Alimentos, Universidad Politécnica de Valencia, Spain
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Sun M, Zhu Y, Jordan B, Wang T. Changes in Physiological Indices, Amino Acids, and Volatile Compounds in Vitis vinifera L. cv. Pinot Noir under UV-B Radiation and Water Deficit Conditions. Foods 2024; 13:508. [PMID: 38397485 PMCID: PMC10888342 DOI: 10.3390/foods13040508] [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: 01/02/2024] [Revised: 01/18/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
UV-B radiation and water deficit can challenge Pinot noir growth and fruit quality. The aim of this work is to determine the effects of UV-B and water deficit on the physiological indices, amino acids, and volatile compounds of Pinot noir vine and fruit. The results showed that both individual and combined treatments caused a decrease in the leaf SPAD, with the largest amplitude being observed in the combined treatment. Water deficit also decreased the leaf water potential and increased the juice δ13C‱ at harvest, which was the opposite of the latter under UV-B radiation. Interestingly, most of the physiological indices under combined stresses did not show significant changes compared with that under no UV-B and the well-watered control treatment. Moreover, the concentrations of amino acids and volatile compounds in the berries were determined at harvest. The amino acid contents were significantly increased by the combined treatment, particularly proline (Pro), aspartate (Arg), alanine (Ala), and threonine (Thr). There were slight increases in volatile compounds. This research substantially contributed to improve our scientific understanding of UV-B and water deficit responses in an important commercial species. In addition, it highlighted some future research to produce high-quality wines with the anticipated specific characteristics.
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Affiliation(s)
- Meng Sun
- Centre for Viticulture and Oenology, Faculty of Agriculture and Life Sciences, Lincoln University, Christchurch 7647, New Zealand; (M.S.); (Y.Z.); (B.J.)
| | - Yifan Zhu
- Centre for Viticulture and Oenology, Faculty of Agriculture and Life Sciences, Lincoln University, Christchurch 7647, New Zealand; (M.S.); (Y.Z.); (B.J.)
| | - Brian Jordan
- Centre for Viticulture and Oenology, Faculty of Agriculture and Life Sciences, Lincoln University, Christchurch 7647, New Zealand; (M.S.); (Y.Z.); (B.J.)
| | - Tao Wang
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences (Nanjing Botanical Garden Mem. Sun Yat-Sen), Nanjing 210014, China
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3
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Gutiérrez-Escobar R, Aliaño-González MJ, Cantos-Villar E. Variety and year: Two key factors on amino acids and biogenic amines content in grapes. Food Res Int 2024; 175:113721. [PMID: 38128986 DOI: 10.1016/j.foodres.2023.113721] [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: 09/14/2023] [Revised: 11/06/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023]
Abstract
Amino acids have proved to play a key role in the development of volatile compounds present in wine with determining repercussions on the final wine bouquets. Biogenic amines originate from the chemical transformations of amino acids found in various foods, a phenomenon that has given rise to several health-related concerns among consumers. In the present research, the evaluation of two of the most influential factors: variety (genetic) and year (climatic conditions) on these compounds in grapes has been performed. Eight Vitis vinifera varieties have been collected during three years and the content of nineteen amino acids, two biogenic amines, and the ammonium ion has been quantified using the HPLC-PDA technique. The genetic factor has proved to be an influential variable (p-value < 0.05) with mean values of amino acids ranging from 896.89 to 1713.79 mg/L and of biogenic amines ranging from 10.61 to 22.28 mg/L. The climatic conditions have shown to be an influential factor as well (p-value < 0.05), being the low temperatures and rainfall and the high solar radiation favour the development of the amino acid and avoid biogenic amines accumulation in grapes.
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Affiliation(s)
- Rocío Gutiérrez-Escobar
- IFAPA Rancho de la Merced, Consejería de Agricultura, Pesca, Agua y Desarrollo Rural, Junta de Andalucía, Cañada de la Loba, 11471 Jerez de la Frontera, Cádiz, Spain.
| | - María José Aliaño-González
- IFAPA Rancho de la Merced, Consejería de Agricultura, Pesca, Agua y Desarrollo Rural, Junta de Andalucía, Cañada de la Loba, 11471 Jerez de la Frontera, Cádiz, Spain; Department of Analytical Chemistry, Faculty of Sciences, Agrifood Campus of International Excellence (ceiA3), IVAGRO, University of Cadiz, Puerto Real, Cadiz 11510, Spain.
| | - Emma Cantos-Villar
- IFAPA Rancho de la Merced, Consejería de Agricultura, Pesca, Agua y Desarrollo Rural, Junta de Andalucía, Cañada de la Loba, 11471 Jerez de la Frontera, Cádiz, Spain.
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Daviet B, Fournier C, Cabrera-Bosquet L, Simonneau T, Cafier M, Romieu C. Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images. PLANT METHODS 2023; 19:146. [PMID: 38098093 PMCID: PMC10720176 DOI: 10.1186/s13007-023-01125-8] [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: 07/17/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023]
Abstract
BACKGROUND Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions. Second, a tracking algorithm was developed to assign a common label to shapes representing the same berry along the time-series. Training and validation of the methods were performed on challenging image datasets acquired in a robotised high-throughput phenotyping platform. Berries were detected on various genotypes with a F1-score of 91.8%, and segmented with a mean absolute error of 4.1% on their area. Tracking allowed to label and retrieve the temporal identity of more than half of the segmented berries, with an accuracy of 98.1%. This method was used to extract individual growth and colour kinetics of various berries from the same bunch, allowing us to propose the first statistically relevant analysis of berry ripening kinetics, with a time resolution lower than one day. CONCLUSIONS We successfully developed a fully-automated open-source method to detect, segment and track overlapping berries in time-series of grapevine bunch images acquired in laboratory conditions. This makes it possible to quantify fine aspects of individual berry development, and to characterise the asynchrony within the bunch. The interest of such analysis was illustrated here for one cultivar, but the method has the potential to be applied in a high throughput phenotyping context. This opens the way for revisiting the genetic and environmental variations of the ripening dynamics. Such variations could be considered both from the point of view of fruit development and the phenological structure of the population, which would constitute a paradigm shift.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | | | - Maxence Cafier
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Charles Romieu
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
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Zhou X, Liu W, Li K, Lu D, Su Y, Ju Y, Fang Y, Yang J. Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy. Foods 2023; 12:4371. [PMID: 38231878 DOI: 10.3390/foods12234371] [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: 10/20/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/19/2024] Open
Abstract
Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.
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Affiliation(s)
- Xuejian Zhou
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Wenzheng Liu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Kai Li
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Dongqing Lu
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yuan Su
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yanlun Ju
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Yulin Fang
- College of Enology, Northwest A&F University, Yangling 712100, China
| | - Jihong Yang
- College of Enology, Northwest A&F University, Yangling 712100, China
- College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China
- Shaanxi Engineering Research Center for Viti-Viniculture, Yangling 712100, China
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Marín-San Román S, Fernández-Novales J, Cebrián-Tarancón C, Sánchez-Gómez R, Diago MP, Garde-Cerdán T. Application of near-infrared spectroscopy for the estimation of volatile compounds in Tempranillo Blanco grape berries during ripening. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:6317-6329. [PMID: 37195204 DOI: 10.1002/jsfa.12706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/25/2023] [Accepted: 05/17/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The knowledge of volatile compounds concentration in grape berries is very valuable information for the winemaker, since these compounds are strongly involved in the final wine quality, and in consumer acceptance. In addition, it would allow to set the harvest date according to aromatic maturity, to classify grape berries according to their quality and to make wines with different characteristics, among other implications. However, so far, there are no tools that allow the volatile composition to be measured directly on intact berries, either in the vineyard or in the winery. RESULTS In this work, the use of near-infrared (NIR) spectroscopy to estimate the aromatic composition and total soluble solids (TSS) of Tempranillo Blanco grape berries during ripening was evaluated. For this purpose, the spectra in the NIR range (1100-2100 nm) of 240 intact berry samples were acquired in the laboratory. From these same samples, the concentration of volatile compounds was analyzed by thin film-solid-phase microextraction-gas chromatography-mass spectrometry (TF-SPME-GC-MS), and the TSS were quantified by refractometry. These two methods were used as reference methods for model building. Calibration, cross-validation and prediction models were built from spectral data using partial least squares (PLS). Determination coefficients of cross-validation (R2 CV ) above 0.5 were obtained for all volatile compounds, their families, and TSS. CONCLUSIONS These findings support that NIR spectroscopy can be successfully use to estimate the aromatic composition as well as the TSS of intact Tempranillo Blanco berries in a non-destructive, fast, and contactless form, allowing simultaneous determination of technological and aromatic maturities. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Sandra Marín-San Román
- Grupo VIENAP, Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain
| | - Juan Fernández-Novales
- Grupo TELEVITIS, Instituto de Ciencias de la Vid y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Logroño, Spain
- Departamento de Agricultura y Alimentación, Universidad de La Rioja, Logroño, Spain
| | - Cristina Cebrián-Tarancón
- Cátedra de Química Agrícola, E.T.S. de Ingeniería Agronómica y de Montes y Biotecnología, Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Albacete, Spain
| | - Rosario Sánchez-Gómez
- Cátedra de Química Agrícola, E.T.S. de Ingeniería Agronómica y de Montes y Biotecnología, Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Albacete, Spain
| | - María Paz Diago
- Grupo TELEVITIS, Instituto de Ciencias de la Vid y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Logroño, Spain
- Departamento de Agricultura y Alimentación, Universidad de La Rioja, Logroño, Spain
| | - Teresa Garde-Cerdán
- Grupo VIENAP, Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Logroño, Spain
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Shi J, Cai H, Qin Z, Li X, Yuan S, Yue X, Sui Y, Sun A, Cui J, Zuo J, Wang Q. Ozone micro-nano bubble water preserves the quality of postharvest parsley. Food Res Int 2023; 170:113020. [PMID: 37316085 DOI: 10.1016/j.foodres.2023.113020] [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: 11/29/2022] [Revised: 05/18/2023] [Accepted: 05/19/2023] [Indexed: 06/16/2023]
Abstract
The production and use of ozone micro-nano bubble water (O3-MNBW) is an innovative technology that prolongs the reactivity of aqueous-phase ozone and maintains the freshness and quality of fruits and vegetables by removing pesticides, mycotoxins, and other contaminants. The quality of parsley treated with different concentrations of O3-MNBW was investigated during storage at 20 ℃ for 5 d, and found that a ten-minute exposure of parsley to 2.5 mg·L-1 O3-MNBW effectively preserved the sensory quality of parsley, and resulted in lower weight loss, respiration rate, ethylene production, MDA levels, and a higher level of firmness, vitamin C, and chlorophyll content, relative to untreated parsley. The O3-MNBW treatment also increased the level of total phenolics and flavonoids, enhanced peroxidase and ascorbate peroxidase activity, and inhibited polyphenol oxidase activity in stored parsley. Five volatile signatures identified using an electronic nose (W1W, sulfur-compounds; W2S, ethanol; W2W, aromatic- and organic- sulfur compounds; W5S, oxynitride; W1S, methane) exhibited a significant decrease in response to the O3-MNBW treatment. A total of 24 major volatiles were identified. A metabolomic analysis identified 365 differentially abundant metabolites (DMs). Among them, 30 and 19 DMs were associated with characteristic volatile flavor substance metabolism in O3-MNBW and control groups, respectively. The O3-MNBW treatment increased the abundance of most DMs related to flavor metabolism and reduced the level of naringin and apigenin. Our results provide insight into the mechanisms that are regulated in response to the exposure of parsley to O3-MNBW, and confirmed the potential use of O3-MNBW as a preservation technology.
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Affiliation(s)
- Junyan Shi
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Department of Food Science and Engineering, College of Biological Sciences and Biotechnology, Beijing Key Laboratory of Forest Food Processing and Safety, Beijing Forestry University, Beijing 100083, China
| | - Huiwen Cai
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Life Sciences, Dalian Minzu University, Dalian 116600, China
| | - Zhanjun Qin
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xiaojiao Li
- School of Biotechnology and Bioengineering, West Yunnan University, Lincang 677000, Yunnan, China
| | - Shuzhi Yuan
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xiaozhen Yue
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Yuan Sui
- Chongqing Key Laboratory of Economic Plant Biotechnology, College of Landscape Architecture and Life Science/Institute of Special Plants, Chongqing University of Arts and Sciences, Yongchuan, Chongqing 402160, China
| | - Aidong Sun
- Department of Food Science and Engineering, College of Biological Sciences and Biotechnology, Beijing Key Laboratory of Forest Food Processing and Safety, Beijing Forestry University, Beijing 100083, China
| | - Jingchun Cui
- College of Life Sciences, Dalian Minzu University, Dalian 116600, China.
| | - Jinhua Zuo
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Qing Wang
- Key Laboratory of the Vegetable Postharvest Treatment of Ministry of Agriculture, Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (North China) of Ministry of Agriculture, Key Laboratory of Urban Agriculture (North) of Ministry of Agriculture, National Engineering Research Center for Vegetables, Beijing Vegetable Research Center, Institute of Agri-food Processing and Nutrition (IAPN), Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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8
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Wu J, Zareef M, Chen Q, Ouyang Q. Application of visible-near infrared spectroscopy in tandem with multivariate analysis for the rapid evaluation of matcha physicochemical indicators. Food Chem 2023; 421:136185. [PMID: 37099951 DOI: 10.1016/j.foodchem.2023.136185] [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] [Received: 10/31/2022] [Revised: 03/15/2023] [Accepted: 04/15/2023] [Indexed: 04/28/2023]
Abstract
Consumer preference for matcha is heavily influenced by its physicochemical properties. The visible-near infrared (Vis-NIR) spectroscopy technology coupled with multivariate analysis was investigated for rapid and non-invasive evaluation of particle size and the ratio of tea polyphenols to free amino acids (P/F ratio) of matcha. The multivariate selection algorithms such as synergy interval (Si), variable combination population analysis (VCPA), competitive adaptive reweighted sampling (CARS), and interval combination population analysis (ICPA) were compared, and eventually, the variable selection strategy of ICPA and CARS hybridization was firstly proposed for selecting the characteristic wavelengths from Vis-NIR spectra to build partial least squares (PLS) models. Results indicated that the ICPA-CARS-PLS models achieved satisfactory performance for the evaluation of matcha particle size (Rp = 0.9376) and P/F ratio (Rp = 0.9283). Hence the rapid, effectual, and nondestructive online monitoring, Vis-NIR reflectance spectroscopy in tandem with chemometric models is significant for the industrial production of matcha.
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Affiliation(s)
- Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China.
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, P.R. China.
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9
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Hubli GB, Banerjee S, Rathore AS. Near-infrared spectroscopy based monitoring of all 20 amino acids in mammalian cell culture broth. Talanta 2023; 254:124187. [PMID: 36549134 DOI: 10.1016/j.talanta.2022.124187] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL-1), lactate (0-2.7 mg mL-1), and trastuzumab (0-2.5 mg mL-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (Rc2 = 0.94-0.99) and prediction (Rp2 = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (REV2 = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.
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Affiliation(s)
| | - Shantanu Banerjee
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.
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10
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Marín-San Román S, Fernández-Novales J, Cebrián-Tarancón C, Sánchez-Gómez R, Diago MP, Garde-Cerdán T. Monitorization of Varietal Aroma Composition Dynamics during Ripening in Intact Vitis vinifera L. Tempranillo Blanco Berries by Hyperspectral Imaging. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:2616-2627. [PMID: 36700632 PMCID: PMC9912339 DOI: 10.1021/acs.jafc.2c07425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/13/2023] [Accepted: 01/13/2023] [Indexed: 06/17/2023]
Abstract
The measurement of aromatic maturity during grape ripening provides very important information for determining the harvest date, particularly in white cultivars. However, there are currently no tools that allow this measurement to be carried out in a noninvasive and rapid way. For this reason, in the present work, we have studied the use of hyperspectral imaging (HSI)) to estimate the aromatic composition of Vitis vinifera L. Tempranillo Blanco berries during ripening. A total of 236 spectra in the VIS+short wave near-infrared (VIS+SW-NIR) range (400-1000 nm) of intact berries were acquired contactless under laboratory conditions. As gold standard values, a total of 20 volatile compounds were quantified by gas chromatography-mass spectrometry (GC-MS), and the concentration of total soluble solids (TSS) was measured by refractometry. Calibration, cross-validation, and prediction models were built using partial least squares (PLS). Values of RCV2 ≥ 0.70 were obtained for α-terpineol, p-cymene, β-damascenone, β-ionone, benzaldehyde, benzyl alcohol, hexanal, citral, linalool, 2-phenylethanol, octanoic acid, nonanoic acid, 2-hexenal, 2-hexen-1-ol, (Z)-3-hexen-1-ol, total C13 norisoprenoids, total C6 compounds, total positive compounds (i.e., the sum of all families except C6 compounds), total benzenoids, and total soluble solids (TSS). Therefore, it can be affirmed that HSI in the VIS + SW-NIR range could be a good tool to estimate the aromatic composition of Tempranillo Blanco grape berries in a contactless, fast, and nondestructive way.
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Affiliation(s)
- Sandra Marín-San Román
- Grupo
VIENAP, Instituto de Ciencias de la Vid
y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Juan Fernández-Novales
- Grupo
TELEVITIS, Instituto de Ciencias de la Vid
y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Cristina Cebrián-Tarancón
- Cátedra
de Química Agrícola, E.T.S.I. Agrónomos y Montes,
Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Avda. de España, s/n, 02071 Albacete, Spain
| | - Rosario Sánchez-Gómez
- Cátedra
de Química Agrícola, E.T.S.I. Agrónomos y Montes,
Departamento de Ciencia y Tecnología Agroforestal y Genética, Universidad de Castilla-La Mancha, Avda. de España, s/n, 02071 Albacete, Spain
| | - Maria Paz Diago
- Grupo
TELEVITIS, Instituto de Ciencias de la Vid
y del Vino (Universidad de La Rioja, CSIC, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
| | - Teresa Garde-Cerdán
- Grupo
VIENAP, Instituto de Ciencias de la Vid
y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja), Ctra. de Burgos, Km. 6, 26007 Logroño, Spain
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11
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Liu X, Li N, Huang Y, Lin X, Ren Z. A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology. FRONTIERS IN PLANT SCIENCE 2023; 13:1084847. [PMID: 36777535 PMCID: PMC9909479 DOI: 10.3389/fpls.2022.1084847] [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: 10/31/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.
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Affiliation(s)
- Xuan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Na Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Yirui Huang
- College of Information Engineering, Hebei GEO University, Shijiazhuang, China
| | - Xiujun Lin
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Zhenhui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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12
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Droukas L, Doulgeri Z, Tsakiridis NL, Triantafyllou D, Kleitsiotis I, Mariolis I, Giakoumis D, Tzovaras D, Kateris D, Bochtis D. A Survey of Robotic Harvesting Systems and Enabling Technologies. J INTELL ROBOT SYST 2023; 107:21. [PMID: 36721646 PMCID: PMC9881528 DOI: 10.1007/s10846-022-01793-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/25/2022] [Indexed: 01/28/2023]
Abstract
This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.
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Affiliation(s)
- Leonidas Droukas
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Zoe Doulgeri
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Nikolaos L. Tsakiridis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki (AUTH), Thessaloniki, 54124 Greece
| | - Dimitra Triantafyllou
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Ioannis Kleitsiotis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Ioannis Mariolis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Giakoumis
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Tzovaras
- Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), Thessaloniki, 57001 Greece
| | - Dimitrios Kateris
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Volos, 38333 Greece
| | - Dionysis Bochtis
- Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology Hellas (CERTH), Volos, 38333 Greece
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13
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RAHMAWATI L, ZAHRA AM, LISTANTI R, MASITHOH RE, HARIADI H, ADNAN, SYAFUTRI MI, LIDIASARI E, AMDANI RZ, PUSPITAHATI, AGUSTINI S, NURAINI L, VOLKANDARI SD, KARIMY MF, SURATNO, WINDARSIH A, PAHLAWAN MFR. Necessity of Log(1/R) and Kubelka-Munk transformation in chemometrics analysis to predict white rice flour adulteration in brown rice flour using visible-near-infrared spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.116422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
| | | | | | | | - Hari HARIADI
- National Research and Innovation Agency, Indonesia
| | - ADNAN
- National Research and Innovation Agency, Indonesia
| | | | | | | | | | - Sri AGUSTINI
- National Research and Innovation Agency, Indonesia
| | | | | | | | - SURATNO
- National Research and Innovation Agency, Indonesia
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14
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RAHMAWATI L, WIDODO S, KURNIADI DP, DAUD P, TRIYONO A, SRIHARTI, SUSANTI ND, MAYASTI NKI, INDRIATI A, YULIANTI LE, PUTRI DP, KUALA SI, ANGGARA CEW, PRISTIANTO EJ, KURNIAWAN ED, APRIYANTO IF, KURNIAWAN D. Determination of colorant type in yellow tofu using Vis-NIR and SW-NIR spectroscopy. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.112422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
| | | | | | | | - Agus TRIYONO
- National Research, and Innovation Agency, Indonesia
| | - SRIHARTI
- National Research, and Innovation Agency, Indonesia
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15
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Ye W, Xu W, Yan T, Yan J, Gao P, Zhang C. Application of Near-Infrared Spectroscopy and Hyperspectral Imaging Combined with Machine Learning Algorithms for Quality Inspection of Grape: A Review. Foods 2022; 12:foods12010132. [PMID: 36613348 PMCID: PMC9818947 DOI: 10.3390/foods12010132] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/06/2022] [Accepted: 12/22/2022] [Indexed: 12/29/2022] Open
Abstract
Grape is a fruit rich in various vitamins, and grape quality is increasingly highly concerned with by consumers. Traditional quality inspection methods are time-consuming, laborious and destructive. Near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are rapid, non-destructive and accurate techniques for quality inspection and safety assessment of agricultural products, which have great potential in recent years. The review summarized the applications and achievements of NIRS and HSI for the quality inspection of grapes for the last ten years. The review introduces basic principles, signal mode, data acquisition, analysis and processing of NIRS and HSI data. Qualitative and quantitative analysis were involved and compared, respectively, based on spectral features, image features and fusion data. The advantages, disadvantages and development trends of NIRS and HSI techniques in grape quality and safety inspection are summarized and discussed. The successful application of NIRS and HSI in grape quality inspection shows that many fruit inspection tasks could be assisted with NIRS and HSI.
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Affiliation(s)
- Weixin Ye
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Wei Xu
- College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Jingkun Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832003, China
- Correspondence: (P.G.); (C.Z.)
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
- Correspondence: (P.G.); (C.Z.)
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16
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Xu M, Sun J, Cheng J, Yao K, Wu X, Zhou X. Non‐destructive prediction of total soluble solids and titratable acidity in Kyoho grape using hyperspectral imaging and deep learning algorithm. Int J Food Sci Technol 2022. [DOI: 10.1111/ijfs.16173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
- School of Electronic Engineering, Changzhou College of Information Technology Changzhou 213164 Jiangsu China
| | - Jun Sun
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Jiehong Cheng
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Kunshan Yao
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xiaohong Wu
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
| | - Xin Zhou
- School of Electrical and Information Engineering Jiangsu University Zhenjiang 212013 Jiangsu China
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17
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Hang J, Shi D, Neufeld J, Bett KE, House JD. Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113669] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning. Foods 2022; 11:foods11111609. [PMID: 35681359 PMCID: PMC9180647 DOI: 10.3390/foods11111609] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 12/25/2022] Open
Abstract
Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.
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19
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Fermentation process monitoring of broad bean paste quality by NIR combined with chemometrics. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01392-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Boido E, Fariña L, Carrau F, Cozzolino D, Dellacassa E. Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes. Food Chem 2022; 387:132927. [PMID: 35421644 DOI: 10.1016/j.foodchem.2022.132927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/04/2022] [Accepted: 04/07/2022] [Indexed: 12/15/2022]
Abstract
Grape variety, vinification, and ageing are factors conditioning the aroma of a wine, with volatile secondary metabolites responsible for the so-called grape varietal character. Particularly, grape glycosylated norisoprenoids are mostly responsible for the sensory profile of Tannat wines, making relevant the use of fast instrumental tools to evaluate their concentration, allow classifying grapes and defining the optimum maturity for harvest. NIR spectroscopy is a fast, non-destructive technique, which requires minimal sample preparation. However, its quantitative applications need chemometric models for interpretation. In this work, a NIR-ANN calibration was developed to quantify norisoprenoids in Vitis vinifera cv. Tannat grapes during maturation and harvesting. Glycosidated norisoprenoids were determined by GC-MS. The ANN adjustments showed better performance than linear models such as PLS, while the best calibration was obtained by homogenising grape samples when comparing to grape juice; making possible to fit a model with an error of 146 μg/kg.
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Affiliation(s)
- Eduardo Boido
- Área de Enología y Biotecnología de las Fermentaciones, Facultad de Química, UdelaR, Uruguay.
| | - Laura Fariña
- Área de Enología y Biotecnología de las Fermentaciones, Facultad de Química, UdelaR, Uruguay
| | - Francisco Carrau
- Área de Enología y Biotecnología de las Fermentaciones, Facultad de Química, UdelaR, Uruguay
| | - Daniel Cozzolino
- Centre for Nutrition and Food Sciences, The University of Queensland, Australia
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21
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Xu M, Sun J, Yao K, Wu X, Shen J, Cao Y, Zhou X. Nondestructive detection of total soluble solids in grapes using VMD-RC and hyperspectral imaging. J Food Sci 2021; 87:326-338. [PMID: 34940982 DOI: 10.1111/1750-3841.16004] [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: 07/25/2021] [Revised: 11/01/2021] [Accepted: 11/12/2021] [Indexed: 11/27/2022]
Abstract
Total soluble solids (TSS) are one of the most essential attributes determining the quality and price of fruit. This study aimed to use hyperspectral imaging (HSI) and wavelength selection for nondestructive detection of TSS in grape. A novel method involving variational mode decomposition and regression coefficients (VMD-RC) was proposed to select feature wavelengths. VMD was introduced to decompose the hyperspectral images data of samples with bands of (400.68-1001.61 nm) to get a series of feature components. Afterward, these components were processed separately using regression analysis to obtain the stability values of RC of each component. Finally, a filter-based selection strategy was used to screen key wavelengths. The least squares support vector machine (LSSVM) and partial least squares (PLS) were constructed under full and feature wavelengths for predicting TSS. The VMD-RC-LSSVM model obtained the best prediction accuracy for TSS, with R p 2 of 0.93, with R M S E P of 0.6 %, with R E R of 18.14 and R P D p of 3.69. The overall results indicated that the VMD-RC algorithm can be used as an alternative to handle high-dimensional hyperspectral images data, and HSI has great potential for nondestructive and rapid evaluation of quality attributes in fruit. PRACTICAL APPLICATION: Traditional methods of evaluating grape quality attributes are destructive, time-consuming and laborious. Therefore, HSI was used to achieve rapid and nondestructive determination of TSS in grape. The results indicated that it was feasible to use HSI and variable selection for predicting TSS. It is of great significance to improve grape quality, guide postharvest handling and provide a valuable reference for noninvasively evaluating other internal attributes of fruit.
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Affiliation(s)
- Min Xu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.,School of Electronic Engineering, Changzhou College of Information Technology, Changzhou, Jiangsu, 213164, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Kunshan Yao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Jifeng Shen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Yan Cao
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
| | - Xin Zhou
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China
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22
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Kusumiyati, Hadiwijaya Y, Putri IE, Munawar AA. Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy. Heliyon 2021; 7:e07677. [PMID: 34401571 PMCID: PMC8353486 DOI: 10.1016/j.heliyon.2021.e07677] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022] Open
Abstract
Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.
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Affiliation(s)
- Kusumiyati
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Yuda Hadiwijaya
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Ine Elisa Putri
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Agus Arip Munawar
- Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Indonesia
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23
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Levate Macedo L, da Silva Araújo C, Costa Vimercati W, Gherardi Hein PR, Pimenta CJ, Henriques Saraiva S. Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:3500-3507. [PMID: 33274765 DOI: 10.1002/jsfa.10981] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 11/20/2020] [Accepted: 12/04/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded. RESULTS The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively. CONCLUSION The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Leandro Levate Macedo
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | - Cintia da Silva Araújo
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | - Wallaf Costa Vimercati
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
| | | | | | - Sérgio Henriques Saraiva
- Department of Food Engineering, Center of Agrarian Sciences and Engineering, Federal University of Espírito Santo, Alegre, Brazil
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24
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van Wyngaard E, Blancquaert E, Nieuwoudt H, Aleixandre-Tudo JL. Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs. FRONTIERS IN PLANT SCIENCE 2021; 12:723247. [PMID: 34539716 PMCID: PMC8448193 DOI: 10.3389/fpls.2021.723247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/09/2021] [Indexed: 05/12/2023]
Abstract
The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.
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Affiliation(s)
- Elizma van Wyngaard
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Erna Blancquaert
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Hélène Nieuwoudt
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
| | - Jose Luis Aleixandre-Tudo
- South African Grape and Wine Research Institute (SAGWRI), Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa
- Instituto de Ingeniería de Alimentos para el Desarrollo (IIAD), Departamento de Tecnologia de Alimentos, Universidad Politécnica de Valencia, Valencia, Spain
- *Correspondence: Jose Luis Aleixandre-Tudo,
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Wang S, Liu X, Tamura T, Kyouno N, Zhang H, Chen JY. Rapid Detection of the Quality of Miso (Japanese Fermented Soybean Paste) Using Visible/Near-Infrared Spectroscopy. ANAL LETT 2020. [DOI: 10.1080/00032719.2020.1858092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Shuo Wang
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan
- College of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu, China
| | - Xiaofang Liu
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan
- College of Food Science and Engineering, Yangzhou University, Yangzhou, Jiangsu, China
| | - Takehiro Tamura
- Akita Prefectural Federation of Miso and Soy Sauce Manufacturers Cooperatives, Akita, Japan
| | - Nobuyuki Kyouno
- Akita Prefectural Federation of Miso and Soy Sauce Manufacturers Cooperatives, Akita, Japan
| | - Han Zhang
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan
| | - Jie Yu Chen
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita, Japan
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Sharabiani VR, Sabzi S, Pourdarbani R, Solis-Carmona E, Hernández-Hernández M, Hernández-Hernández JL. Non-Destructive Prediction of Titratable Acidity and Taste Index Properties of Gala Apple Using Combination of Different Hybrids ANN and PLSR-Model Based Spectral Data. PLANTS 2020; 9:plants9121718. [PMID: 33291348 PMCID: PMC7762319 DOI: 10.3390/plants9121718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/30/2020] [Accepted: 12/02/2020] [Indexed: 12/14/2022]
Abstract
Non-destructive estimation of the internal properties of fruits and vegetables is very important, because better management can be provided for subsequent operations. Researchers and scientists around the world are focusing on non-destructive methods because if they are developed and commercialized, there will be an impressive change in the food industry. In this regard, this paper aims to present a non-destructive method based on Vis-NIR spectral data. The different stages of the proposed algorithm are: (1) Collection of samples of Gala apples, (2) Spectral data extraction by spectroscopy, (3) Pre-processing of spectral data, (4) Measurement of chemical properties of titratable acidity (TA) and taste index, (5) Selection of key wavelengths using hybrid artificial neural network-firefly algorithm (ANN-FA), (6) Non-destructive estimation of the properties using two methods of hybrid ANN- Particle swarm optimization algorithm and partial least squares regression. For considering the reliability of methods for estimating the chemical properties, the prediction operation was executed in 300 iterations. The results represented that the mean and standard deviation of the correlation coefficient and the root mean square error of hybrid ANN-PSO and PLSR for TA were 0.9095 ± 0.0175, 0.0598 ± 0.0064, 0.834 ± 0.0313 and 0.0761 ± 0.0061 respectively. These values for taste index were 0.918 ± 0.02, 3.2 ± 0.39, 0.836 ± 0.033 and 4.09 ± 0.403, respectively. Therefore, it can be concluded that the hybrid ANN-PSO has a better performance for non-destructive prediction of the two mentioned chemical properties than the PLSR method. In general, the proposed method can predict the chemical properties of TA and taste index non-destructively, which is very useful for mechanized harvesting and management of post-harvest operation.
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Affiliation(s)
- Vali Rasooli Sharabiani
- Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh, Ardabili 56199-11367, Iran; (S.S.); (R.P.)
- Correspondence: (V.R.S.); (J.L.H.-H.)
| | - Sajad Sabzi
- Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh, Ardabili 56199-11367, Iran; (S.S.); (R.P.)
| | - Razieh Pourdarbani
- Department of Biosystem Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh, Ardabili 56199-11367, Iran; (S.S.); (R.P.)
| | - Edgardo Solis-Carmona
- Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39087, Mexico; (E.S.-C.); (M.H.-H.)
| | - Mario Hernández-Hernández
- Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39087, Mexico; (E.S.-C.); (M.H.-H.)
| | - José Luis Hernández-Hernández
- Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo 39087, Mexico; (E.S.-C.); (M.H.-H.)
- Division of Research and Graduate Studies, TecNM/Technological Institute of Chilpancingo, Chilpancingo 39070, Mexico
- Correspondence: (V.R.S.); (J.L.H.-H.)
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Pourdarbani R, Sabzi S, Hernández-Hernández M, Hernández-Hernández JL, Gallardo-Bernal I, Herrera-Miranda I. Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network-Imperialist Competitive Algorithm. PLANTS (BASEL, SWITZERLAND) 2020; 9:plants9111547. [PMID: 33198098 PMCID: PMC7696532 DOI: 10.3390/plants9111547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 10/27/2020] [Accepted: 11/10/2020] [Indexed: 06/11/2023]
Abstract
Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660-720 nm using hybrid artificial neural network-particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively.
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Affiliation(s)
- Razieh Pourdarbani
- Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
| | - Sajad Sabzi
- Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran;
| | | | - José Luis Hernández-Hernández
- Faculty of Engineering, Autonomous University of Guerrero, Chilpancingo, Guerrero 39087, Mexico;
- National Technology of México/Campus Chilpancingo, Chilpancingo, Guerrero 39070, Mexico
| | - Iván Gallardo-Bernal
- Higher School of Government and Public Management, Autonomous University of Guerrero, Chilpancingo, Guerrero 39087, Mexico;
| | - Israel Herrera-Miranda
- Government and Public Management Faculty, Autonomous University of Guerrero, Chilpancingo, Guerrero 39087, Mexico;
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Song Z, Li H, Wen J, Zeng Y, Ye X, Zhao W, Xu T, Xu N, Zhang D. Consumers' attention on identification, nutritional compounds, and safety in heavy metals of Canadian sea cucumber in Chinese food market. Food Sci Nutr 2020; 8:5962-5975. [PMID: 33282248 PMCID: PMC7684582 DOI: 10.1002/fsn3.1882] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 07/13/2020] [Accepted: 07/14/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Based on the consumers' attention issues of sea cucumbers, we aimed to complete comprehensive information of commercial Canadian sea cucumbers (CCSC), which sprang up extensively in Chinese food market. RESULTS CCSC were identified as Cucumaria frondosa and characterized based on the characteristics, nutritional compositions, and heavy metals. The abdomen and five internal tendons of Cucumaria frondosa were special orange. The average of soaking degree and water content, which consumers paid great attention to, was 2.8 ± 0.3 and 0.46 ± 0.09%, respectively. Proteins (56.4 ± 9.1%) and polysaccharides (12.2 ± 14.7%) were the principal nutrient component. In addition, there was a variety of free amino acids, in which arginine (70.1 ± 50.0 mg/100 g), glutamate (42.6 ± 23.9 mg/100 g), and alanine (32.2 ± 21.0 mg/100 g) were the main components. Phosphorus (P, 0.26 ± 0.05%), magnesium (Mg, 0.19 ± 0.07%), and kalium (K, 0.17 ± 0.08%) were the major mineral elements. Amount of heavy metal was within the safety limitation (5.5 ± 1.4 mg/kg). Furthermore, the active ingredients were positively correlated with size. CONCLUSION The overall findings enriched the information of Cucumaria frondosa for consumers and suggested that the quality of Cucumaria frondosa was varied following commercial classification and size.
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Affiliation(s)
- Zhuoyue Song
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
| | - Hailun Li
- Department of NephrologyAffiliated Huai'an Hospital of Xuzhou Medical UniversityHuai'anChina
| | - Jing Wen
- Department of BiologyLingnan Normal UniversityZhanjiangChina
| | - Yeda Zeng
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
| | - Xianying Ye
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
| | - Weibo Zhao
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
| | - Tingting Xu
- Jiangsu Key Laboratory of Regional Resource Exploitation and Medicinal ResearchHuaiyin Institute of TechnologyHuai'anChina
| | - Nenggui Xu
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
| | - Danyan Zhang
- Clinical Medical College of Acupuncture Moxibustion and RehabilitationSchool of Pharmaceutical ScienceGuangzhou University of Chinese MedicineGuangzhouChina
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Yi Y, Hua H, Sun X, Guan Y, Chen C. Rapid determination of polysaccharides and antioxidant activity of Poria cocos using near-infrared spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118623. [PMID: 32599484 DOI: 10.1016/j.saa.2020.118623] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 06/11/2023]
Abstract
This study evaluates the feasibility of near-infrared (NIR) spectroscopy combined with chemometrics as a fast and efficient technique to predict the polysaccharide content and antioxidant activity of Poria cocos. The reference values of polysaccharide content were determined by the phenol-sulfuric acid method, and the antioxidant activities were determined by the DPPH scavenge assay, FRAP scavenge assay and ABTS scavenge assay, respectively. The partial least squares regression algorithm was used to relate the spectra to the reference values. Various methods for spectra pretreatment and variable selection were optimized to improve the predictability and stability of the models. As a result, the best models yielded very satisfying results, of which the values of coefficients of determination were all >0.94, and the values of residual predictive deviation were all >4. Such results confirmed that the present method is robust and applicable, and thus has good potential for rapid quality evaluation of Poria cocos.
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Affiliation(s)
- Yuan Yi
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Haimin Hua
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Xuefen Sun
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Ying Guan
- School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou 510006, PR China
| | - Chao Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; Key Laboratory of Digitalization Quality Evaluation of Chinese Materia Medica of SATCM, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica in Guangdong Universities, Guangzhou 510006, PR China; Research Center for Quality Engineering & Technology of Chinese Materia Medica of Guangdong Province, Guangzhou 510006, PR China.
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Billet K, Malinowska MA, Munsch T, Unlubayir M, Adler S, Delanoue G, Lanoue A. Semi-Targeted Metabolomics to Validate Biomarkers of Grape Downy Mildew Infection Under Field Conditions. PLANTS (BASEL, SWITZERLAND) 2020; 9:E1008. [PMID: 32784974 PMCID: PMC7465342 DOI: 10.3390/plants9081008] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 07/30/2020] [Accepted: 08/06/2020] [Indexed: 12/26/2022]
Abstract
Grape downy mildew is a devastating disease worldwide and new molecular phenotyping tools are required to detect metabolic changes associated to plant disease symptoms. In this purpose, we used UPLC-DAD-MS-based semi-targeted metabolomics to screen downy mildew symptomatic leaves that expressed oil spots (6 dpi, days post-infection) and necrotic lesions (15 dpi) under natural infections in the field. Leaf extract analyses enabled the identification of 47 metabolites belonging to the primary metabolism including 6 amino acids and 1 organic acid, as well as an important diversity of specialized metabolites including 9 flavonols, 11 flavan-3-ols, 3 phenolic acids, and stilbenoids with various degree of polymerization (DP) including 4 stilbenoids DP1, 8 stilbenoids DP2, and 4 stilbenoids DP3. Principal component analysis (PCA) was applied as unsupervised multivariate statistical analysis method to reveal metabolic variables that were affected by the infection status. Univariate and multivariate statistics revealed 33 and 27 metabolites as relevant infection biomarkers at 6 and 15 dpi, respectively. Correlation-based networks highlighted a general decrease of flavonoid-related metabolites, whereas stilbenoid DP1 and DP2 concentrations increased upon downy mildew infection. Stilbenoids DP3 were identified only in necrotic lesions representing late biomarkers of downy mildew infection.
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Affiliation(s)
- Kévin Billet
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
| | - Magdalena Anna Malinowska
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
- Faculty of Chemical Engineering and Technology, Cracow University of Technology, 24 Warszawska St., 31-155 Cracow, Poland
| | - Thibaut Munsch
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
| | - Marianne Unlubayir
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
| | - Sophie Adler
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
| | - Guillaume Delanoue
- Institut Français de la Vigne et du Vin, 509 avenue Chanteloup, F37400 Amboise, France;
| | - Arnaud Lanoue
- EA2106 “Biomolécules et Biotechnologies Végétales”, UFR des Sciences Pharmaceutiques “Philippe Maupas”, Université de Tours, 31 av. Monge, F37200 Tours, France; (K.B.); (M.A.M.); (T.M.); (M.U.); (S.A.)
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Ferrer-Gallego R, Rodríguez-Pulido FJ, Toci AT, García-Estevez I. Phenolic Composition, Quality and Authenticity of Grapes and Wines by Vibrational Spectroscopy. FOOD REVIEWS INTERNATIONAL 2020. [DOI: 10.1080/87559129.2020.1752231] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
| | - Francisco J. Rodríguez-Pulido
- Food Colour & Quality Laboratory, Department Nutrition & Food Science, Facultad de Farmacia, Universidad de Sevilla, Sevilla, Spain
| | - Aline T. Toci
- Environmental and Food Interdisciplinary Studies Laboratory, Federal University of Latin American Integration (UNILA), Foz do Iguaçú, Brazil
| | - Ignacio García-Estevez
- Grupo de Investigación en Polifenoles, Departamento Química Analítica, Nutrición y Bromatología, Facultad de Farmacia, Universidad de Salamanca, Salamanca, Spain
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Valorization of sage extracts (Salvia officinalis L.) obtained by high voltage electrical discharges: Process control and antioxidant properties. INNOV FOOD SCI EMERG 2020. [DOI: 10.1016/j.ifset.2019.102284] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Huang Y, Ren Z, Li D, Liu X. Phenotypic techniques and applications in fruit trees: a review. PLANT METHODS 2020; 16:107. [PMID: 32782454 PMCID: PMC7412798 DOI: 10.1186/s13007-020-00649-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 07/30/2020] [Indexed: 05/03/2023]
Abstract
Phenotypic information is of great significance for irrigation management, disease prevention and yield improvement. Interest in the evaluation of phenotypes has grown with the goal of enhancing the quality of fruit trees. Traditional techniques for monitoring fruit tree phenotypes are destructive and time-consuming. The development of advanced technology is the key to rapid and non-destructive detection. This review describes several techniques applied to fruit tree phenotypic research in the field, including visible and near-infrared (VIS-NIR) spectroscopy, digital photography, multispectral and hyperspectral imaging, thermal imaging, and light detection and ranging (LiDAR). The applications of these technologies are summarized in terms of architecture parameters, pigment and nutrient contents, water stress, biochemical parameters of fruits and disease detection. These techniques have been shown to play important roles in fruit tree phenotypic research.
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Affiliation(s)
- Yirui Huang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001 China
| | - Zhenhui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001 China
| | - Dongming Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001 China
| | - Xuan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001 China
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From the Laboratory to The Vineyard-Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis. High Throughput 2019; 8:ht8040021. [PMID: 31801256 PMCID: PMC6966591 DOI: 10.3390/ht8040021] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/27/2019] [Accepted: 11/27/2019] [Indexed: 11/17/2022] Open
Abstract
Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.
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35
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Cross-flow filtration of lees grape juice for non-aromatic white wine production: a case study on an Italian PDO. Eur Food Res Technol 2019. [DOI: 10.1007/s00217-019-03382-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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