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Li S, Li J, Wang Q, Shi R, Yang X, Zhang Q. Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy. FRONTIERS IN PLANT SCIENCE 2024; 15:1324753. [PMID: 38322826 PMCID: PMC10844474 DOI: 10.3389/fpls.2024.1324753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/08/2024] [Indexed: 02/08/2024]
Abstract
Introduction Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming. Methods To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models. Results The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix). Discussion This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.
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Affiliation(s)
- Sheng Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Jiangbo Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ruiyao Shi
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China
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Zeng S, Zhang Z, Cheng X, Cai X, Cao M, Guo W. Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123402. [PMID: 37738767 DOI: 10.1016/j.saa.2023.123402] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp's reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.
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Affiliation(s)
- Shuochong Zeng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zongyi Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaodong Cheng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiao Cai
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mengke Cao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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Shi X, Song J, Wang H, Lv X, Tian T, Wang J, Li W, Zhong M, Jiang M. Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters. FRONTIERS IN PLANT SCIENCE 2023; 14:1171594. [PMID: 37469774 PMCID: PMC10352918 DOI: 10.3389/fpls.2023.1171594] [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: 02/22/2023] [Accepted: 06/16/2023] [Indexed: 07/21/2023]
Abstract
Soil salinization is one of the main causes of land degradation in arid and semi-arid areas. Timely and accurate monitoring of soil salinity in different areas is a prerequisite for amelioration. Hyperspectral technology has been widely used in soil salinity monitoring due to its high efficiency and rapidity. However, vegetation cover is an inevitable interference in the direct acquisition of soil spectra during crop growth period, which greatly limits the monitoring of soil salinity by remote sensing. Due to high soil salinity could lead to difficulty in plants' water absorption, and inhibit plant dry matter accumulation, a method for monitoring root zone soil salinity by combining vegetation canopy spectral information and crop aboveground growth parameters was proposed in this study. The canopy spectral information was acquired by a spectroradiometer, and then variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RFA) were used to extract the salinity spectral features in cotton canopy spectrum. The extracted features were then used to estimate root zone soil salinity in cotton field by combining with cotton plant height, aboveground biomass, and shoot water content. The results showed that there was a negative correlation between plant height/aboveground biomass/shoot water content and soil salinity in 0-20, 0-40, and 0-60 cm soil layers at different growth stages of cotton. Spectral feature selection by the three methods all improved the prediction accuracy of soil salinity, especially CARS. The prediction accuracy based on the combination of spectral features and cotton growth parameters was significantly higher than that based on only spectral features, with R2 increasing by 10.01%, 18.35%, and 29.90% for the 0-20, 0-40, and 0-60 cm soil layer, respectively. The model constructed based on the first derivative spectral preprocessing, spectral feature selection by CARS, cotton plant height, and shoot water content had the highest accuracy for each soil layer, with R2 of 0.715,0.769, and 0.742 for the 0-20, 0-40, 0-60 cm soil layer, respectively. Therefore, the method by combining cotton canopy hyperspectral data and plant growth parameters could significantly improve the prediction accuracy of root zone soil salinity under vegetation cover conditions. This is of great significance for the amelioration of saline soil in salinized farmlands arid areas.
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Affiliation(s)
- Xiaoyan Shi
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Jianghui Song
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Haijiang Wang
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Xin Lv
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Tian Tian
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Jingang Wang
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Weidi Li
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Mingtao Zhong
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
| | - Menghao Jiang
- College of Agriculture, Shihezi University, Shihezi, China
- Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production and Construction Corps, Shihezi University, Shihezi, Xinjiang, China
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Duckena L, Alksnis R, Erdberga I, Alsina I, Dubova L, Duma M. Non-Destructive Quality Evaluation of 80 Tomato Varieties Using Vis-NIR Spectroscopy. Foods 2023; 12:foods12101990. [PMID: 37238808 DOI: 10.3390/foods12101990] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/07/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
Traditional biochemical methods are resource- and time-consuming; therefore, there is a need for cost-effective alternatives. A spectral analysis is one of the non-destructive techniques that are more widely used for fruit quality determination; however, references are needed for traditional methods. In this study, visible and near-infrared (Vis-NIR) spectroscopy was used to analyze the internal quality attributes of tomatoes. For the first time, 80 varieties with large differences in fruit size, shape, color, and internal structure were used for an analysis. The aim of this study was to develop models suitable to predict a taste index, as well as the content of lycopene, flavonoids, β-carotene, total phenols, and dry matter of intact tomatoes based on Vis-NIR reflectance spectra. The content of phytochemicals was determined in 80 varieties of tomatoes. A total of 140 Vis-NIR reflectance spectra were obtained using the portable spectroradiometer RS-3500 (Spectral Evolution Inc.). Partial least squares regression (PLS) and multiple scatter correction (MSC) were used to develop calibration models. Our results indicated that PLS models with good prediction accuracies were obtained. The present study showed the high capability of Vis-NIR spectroscopy to determine the content of lycopene and dry matter of intact tomatoes with a determination coefficient of 0.90 for both parameters. A regression fit of R2 = 0.86, R2 = 0.84, R2 = 0.82, and R2= 0.73 was also achieved for the taste index, flavonoids, β-carotene, and total phenols, respectively.
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Affiliation(s)
- Lilija Duckena
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Reinis Alksnis
- Department of Mathematics, Faculty of Information Technologies, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ieva Erdberga
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Ina Alsina
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Laila Dubova
- Faculty of Agriculture, Institute of Soil and Plant Science, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
| | - Mara Duma
- Department of Chemistry, Faculty of Food Technology, Latvia University of Life Sciences and Technologies, 2 Liela Street, LV-3001 Jelgava, Latvia
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Zhao S, Adade SYSS, Wang Z, Wu J, Jiao T, Li H, Chen Q. On-line monitoring of total sugar during kombucha fermentation process by near-infrared spectroscopy: Comparison of linear and non-linear multiple calibration methods. Food Chem 2023; 423:136208. [PMID: 37163914 DOI: 10.1016/j.foodchem.2023.136208] [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: 12/17/2022] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 05/12/2023]
Abstract
Kombucha is widely recognized for its health benefits, and it facilitates high-quality transformation and utilization of tea during the fermentation process. Implementing on-line monitoring for the kombucha production process is crucial to promote the valuable utilization of low-quality tea residue. Near-infrared (NIR) spectroscopy, together with partial least squares (PLS), backpropagation neural network (BPANN), and their combination (PLS-BPANN), were utilized in this study to monitor the total sugar of kombucha. In all, 16 mathematical models were constructed and assessed. The results demonstrate that the PLS-BPANN model is superior to all others, with a determination coefficient (R2p) of 0.9437 and a root mean square error of prediction (RMSEP) of 0.8600 g/L and a good verification effect. The results suggest that NIR coupled with PLS-BPANN can be used as a non-destructive and on-line technique to monitor total sugar changes.
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Affiliation(s)
- Songguang Zhao
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Zhen Wang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Jizhong Wu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Tianhui Jiao
- College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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6
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Zhang J, Li Y, Wang B, Song J, Li M, Chen P, Shen Z, Wu Y, Mao C, Cao H, Wang X, Zhang W, Lu T. Rapid evaluation of Radix Paeoniae Alba and its processed products by near-infrared spectroscopy combined with multivariate algorithms. Anal Bioanal Chem 2023; 415:1719-1732. [PMID: 36763106 DOI: 10.1007/s00216-023-04570-5] [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: 11/14/2022] [Revised: 01/07/2023] [Accepted: 01/25/2023] [Indexed: 02/11/2023]
Abstract
It is well known that the processing method of herbal medicine has a complex impact on the active components and clinical efficacy, which is difficult to measure. As a representative herb medicine with diverse processing methods, Radix Paeoniae Alba (RPA) and its processed products differ greatly in clinical efficacy. However, in some cases, different processed products are confused for use in clinical practice. Therefore, it is necessary to strictly control the quality of RPA and its processed products. Giving that the time-consuming and laborious operation of traditional quality control methods, a comprehensive strategy of near-infrared (NIR) spectroscopy combined with multivariate algorithms was proposed. This strategy has the advantages of being rapid and non-destructive, not only qualitatively distinguishing RPA and various processed products but also enabling quantitative prediction of five bioactive components. Qualitatively, the subspace clustering algorithm successfully differentiated RPA and three processed products, with an accuracy rate of 97.1%; quantitatively, interval combination optimization (ICO), competitive adaptive reweighted sampling (CARS), and competitive adaptive reweighted sampling combined with successive projections algorithm (CARS-SPA) were used to optimize the PLS model, and satisfactory results were obtained in terms of wavelength selection. In conclusion, it is feasible to use NIR spectroscopy to rapidly evaluate the effect of processing methods on the quality of RPA, which provides a meaningful reference for quality control of other herbal medicines with numerous processing methods.
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Affiliation(s)
- Jiuba Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Yu Li
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Bin Wang
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Jiantao Song
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Mingxuan Li
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Peng Chen
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Zheyuan Shen
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Yi Wu
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Chunqin Mao
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Hui Cao
- Research Center for Traditional Chinese Medicine of Lingnan (Southern China), Jinan University, Guangzhou, 510632, China
| | - Xiachang Wang
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China
| | - Wei Zhang
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China. .,College of Pharmacy, Anhui University of Chinese Medicine, Hefei, 230038, China. .,Anhui Province Key Laboratory of Traditional Chinese Medicine Decoction Pieces of New Manufacturing Technology, Hefei, 230038, China.
| | - Tulin Lu
- College of Pharmacy, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, People's Republic of China.
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ZENG S, WU X, WU B, ZHOU H, WANG M. Rapid determination of cadmium residues in tomato leaves by Vis-NIR hyperspectral and Synergy interval PLS coupled Monte Carlo method. FOOD SCIENCE AND TECHNOLOGY 2023. [DOI: 10.1590/fst.113422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | - Xiaohong WU
- Jiangsu University, China; Jiangsu University, China
| | - Bin WU
- Chuzhou Polytechnic, China
| | - Haoxiang ZHOU
- Research Institute of Zhejiang University-Taizhou, China
| | - Meng WANG
- Research Institute of Zhejiang University-Taizhou, China
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Hao Y, Li X, Zhang C, Lei Z. Research on construction method and validity mechanism of robust analysis model in snow peach quality detection based on visible-near infrared spectroscopy. Front Nutr 2022; 9:1042868. [DOI: 10.3389/fnut.2022.1042868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/03/2022] [Indexed: 11/13/2022] Open
Abstract
Visible-near infrared (Vis-NIR) spectra analysis method is widely used in the quality grading of bulk fruits with its rapid, non-destructive, diverse detection modes and flexible modular integration scheme. However, during the online grading of fruits, the random mechanized way of dropping fruit onto the conveyor belt method and the open detection environment led to more spectral abnormal samples, which affect the accuracy of the detection. In this paper, the soluble solids content (SSC) of snow peach is quantitatively analyzed by static and online detection methods. Several spectral preprocessing methods including Norris-Williams Smoothing (NWS), Savitzky-Golay Smoothing (SGS), Continuous Wavelet Derivative (CWD), Multivariate Scattering Correction (MSC), and Variable Sorting for Normalization (VSN) are adopted to eliminate spectral rotation and translation errors and improve the signal-to-noise ratio. Monte Carlo Uninformative Variable Elimination (MCUVE) method is used for the selection of optimal characteristic modeling variables. Partial Least Squares Regression (PLSR) is used to model and analyze the preprocessed spectra and the spectral variables optimized by MCUVE, and the effectiveness of the method is evaluated. Sparse Partial Least Squares Regression (SPLSR) and Sparse Partial Robust M Regression (SPRMR) are used for the construction of robust models. The results showed that the SGS preprocessing method can effectively improve the analysis accuracy of static and online models. The MCUVE method can realize the extraction of stable characteristic variables. The SPRMR model based on SGS preprocessing method and the effective variables has the optimal analysis results. The analysis accuracy of snow peach static model is slightly better than that of online analytical model. Through the test results of the PLSR, SPLSR and SPRMR models by the artificially adding noise test method, it can be seen that the SPRMR method eliminates the influence of abnormal samples on the model during the modeling process, which can effectively improve the anti-noise ability and detection reliability.
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Sringarm C, Numthuam S, Singanusong R, Jiamyangyuen S, Kittiwatchana S, Funsueb S, Rungchang S. Quantitative determination of quality control parameters using near infrared spectroscopy and chemometrics in process monitoring of tapioca sweetener production. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Chen Y, Sun W, Jiu S, Wang L, Deng B, Chen Z, Jiang F, Hu M, Zhang C. Soluble Solids Content Binary Classification of Miyagawa Satsuma in Chongming Island Based on Near Infrared Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:841452. [PMID: 35923875 PMCID: PMC9340214 DOI: 10.3389/fpls.2022.841452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Citrus is one of the most important fruits in China. Miyagawa Satsuma, one kind of citrus, is a nutritious agricultural product with regional characteristics of Chongming Island. Near-infrared Spectroscopy (NIR) is a proper method for studying the quality of fruits, because it is low-cost, efficient, non-destructive, and repeatable. Therefore, the NIR technique is used to detect citrus's soluble solid content (SSC) in this study. After obtaining the original spectral data, the first 70% of them are divided into the training set and 30% into the test set. Then, the Random Frog algorithm is chosen to select characteristic wavelengths, which reduces the dimension of the data and the complexity of the model, and accordingly makes the generalization of the classification model better. After comparing the performance of various classifiers (AdaBoost, KNN, LS-SVM, and Bayes) under different characteristic wavelength numbers, the AdaBoost classifier outperforms using 275 characteristic wavelengths for modeling eventually. The accuracy, precision, recall, and F 1-score are 78.3%, 80.5%, 78.3%, and 0.780, respectively and the ROC (Receiver Operating Characteristic Curve, ROC curve) is close to the upper left corner, suggesting that the classification model is acceptable. The results demonstrate that it is feasible to use the NIR technique to estimate whether the citrus is sweet or not. Furthermore, it is beneficial for us to apply the obtained models for identifying the quality of citrus correctly. For fruit traders, the model helps them to determine the growth cycle of citrus more scientifically, improve the level of citrus cultivation and management and the final fruit quality, and thus increase the economic income of fruit traders.
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Affiliation(s)
- Yuzhen Chen
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Wanxia Sun
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Songtao Jiu
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Wang
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Bohan Deng
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zili Chen
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Jiang
- Shanghai Citrus Research Institute, Shanghai, China
| | - Menghan Hu
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Caixi Zhang
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
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Li L, Lu L, Zhao X, Hu D, Tang T, Tang Y. Nondestructive detection of tomato quality based on multiregion combination model. J FOOD PROCESS ENG 2022. [DOI: 10.1111/jfpe.14100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Li Li
- School of Physics Guizhou University Guiyang China
| | - Li‐Min Lu
- School of Physics Guizhou University Guiyang China
| | | | - De‐Yuan Hu
- School of Physics Guizhou University Guiyang China
| | - Tian‐Yu Tang
- School of Physics Guizhou University Guiyang China
| | - Yan‐Lin Tang
- School of Physics Guizhou University Guiyang China
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12
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Zhang Y, Huang J, Zhang Q, Liu J, Meng Y, Yu Y. Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system. APPLIED OPTICS 2022; 61:3419-3428. [PMID: 35471438 DOI: 10.1364/ao.455024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
The soluble solids content (SSC) is an important factor in the internal quality detection of apples. It is essential to have reliable and high-speed measurement of the SSC. However, almost all traditional equipment is inconvenient and expensive. We designed a handheld nondestructive SSC detector based on near-infrared (NIR) spectroscopy, which is composed of a portable NIR spectrometer, cloud server, smartphone app, and prediction model of SSC. We preprocessed the spectrum with multiplicative scatter correction (MSC), standard normal variable transformation (SNV), and Savitzky-Golay (S-G) smoothing algorithms. Besides, the linear weight reduction of the particle swarm optimization algorithm is carried out, and we establish the model of an extreme learning machine optimized with the improved particle swarm optimization (IPSO-ELM) algorithm. The R2, root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) of the model are 0.993, 0.0155, and 11.6, respectively, which are better than the traditional model obviously. In addition, the number of wavelengths reduced from 228 to 70 as the model is simplified with the uninformative variable elimination (UVE) method. The time of training is reduced by 29.30% compared with the original spectrum. It can be verified that the IPSO-ELM model has good prediction performance, and the NIR diffuse reflectance spectroscopy is a reliable nondestructive measurement of SSC in apples.
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Non-destructive determination of color, titratable acidity, and dry matter in intact tomatoes using a portable Vis-NIR spectrometer. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2021.104288] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Dong X, Yan X, Qu H. Advanced process control for salvianolic acid A conversion reaction based on data-driven and mechanism-driven model. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Zhang M, Shen M, Li H, Zhang B, Zhang Z, Quan P, Ren X, Xing L, Zhao J. Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 267:120598. [PMID: 34802937 DOI: 10.1016/j.saa.2021.120598] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/25/2021] [Accepted: 11/06/2021] [Indexed: 06/13/2023]
Abstract
In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm2, respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm2 respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.
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Affiliation(s)
- Mengsheng Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Maosheng Shen
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Hao Li
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Bo Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Zhongxiong Zhang
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
| | - Pengkun Quan
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China
| | - Xiaolin Ren
- Northwest A&F University, College of Horticulture, Yangling, Shaanxi 712100, China
| | - Libo Xing
- Northwest A&F University, College of Horticulture, Yangling, Shaanxi 712100, China
| | - Juan Zhao
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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Hu Y, Kang Z. The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27041196. [PMID: 35208985 PMCID: PMC8876823 DOI: 10.3390/molecules27041196] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 11/16/2022]
Abstract
Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.
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OUP accepted manuscript. J Pharm Pharmacol 2022; 74:1040-1050. [DOI: 10.1093/jpp/rgab177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022]
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