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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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2
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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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Song L, Chen E, Zheng T, Li J, Wang H, Zhu X. Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 276:121214. [PMID: 35395464 DOI: 10.1016/j.saa.2022.121214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accurately extract the feature of the spectral data, which will lead to missing detection or false detection; when using deep learning to build a qualitative detection model, due to interference of the external environment and other factors, the spectral data collected may have outliers, this means that the knowledge generalization on anomalous testing data, which may have a different distribution of that of the training set, is not trivial, which will also lead to missing detection or false detection. To solve the above problems, this paper proposes a novel qualitative detection neural network by analyzing the near infrared spectral data of blend fabrics. Firstly, we remove the convolutional layer and pooling layer of the CNN, making full use of the feature to enhance the feature representation ability of the model. Secondly, adding the L1 norm of the feature coefficients as a penalty term to the loss function to force those features with high redundancy to become weaker. Thirdly, in order to improve the recognition accuracy of the anomalous spectral data and minimize the model uncertainty, an ensemble machine learning approach utilizing 5 neural networks in parallel is used. To show the superiority of our proposed method, the existing methods are used as competitive methods to compare with our method. Our homemade dataset contains 3482 samples of blend fabrics with 9 different compositions. The results show that the Micro-F1-score, Micro-Specificity, Weight-F1-score, and Weight-Specificity of this method respectively 99.71%, 99.96%, 99.73%, and 99.99%, the results further confirm the method has higher analysis accuracy and stability. In addition, the method proposed in this paper can greatly improve the recognition accuracy of the anomalous spectral data. It has important practical value in the qualitative detection of blend fabrics.
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Affiliation(s)
- Limei Song
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China.
| | - Enze Chen
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Tenglong Zheng
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Jinyi Li
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Hongyi Wang
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Xinjun Zhu
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of 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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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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Ai N, Jiang Y, Omar S, Wang J, Xia L, Ren J. Rapid Measurement of Cellulose, Hemicellulose, and Lignin Content in Sargassum horneri by Near-Infrared Spectroscopy and Characteristic Variables Selection Methods. Molecules 2022; 27:335. [PMID: 35056650 PMCID: PMC8780011 DOI: 10.3390/molecules27020335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 12/31/2021] [Indexed: 11/17/2022] Open
Abstract
Near-infrared (NIR) spectroscopy and characteristic variables selection methods were used to develop a quick method for the determination of cellulose, hemicellulose, and lignin contents in Sargassum horneri. Calibration models for cellulose, hemicellulose, and lignin in Sargassum horneri were established using partial least square regression methods with full variables (full-PLSR). The PLSR calibration models were established by four characteristic variables selection methods, including interval partial least square (iPLS), competitive adaptive reweighted sampling (CARS), correlation coefficient (CC), and genetic algorithm (GA). The results showed that the performance of the four calibration models, namely iPLS-PLSR, CARS-PLSR, CC-PLSR, and GA-PLSR, was better than the full-PLSR calibration model. The iPLS method was best in the performance of the models. For iPLS-PLSR, the determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the prediction set were as follows: 0.8955, 0.8232%, and 3.0934 for cellulose, 0.8669, 0.4697%, and 2.7406 for hemicellulose, and 0.7307, 0.7533%, and 1.9272 for lignin, respectively. These findings indicate that the NIR calibration models can be used to predict cellulose, hemicellulose, and lignin contents in Sargassum horneri quickly and accurately.
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Affiliation(s)
- Ning Ai
- College of Biological, Chemical Science and Engineering, Jiaxing University, Jiaxing 314001, China;
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
| | - Yibo Jiang
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Sainab Omar
- Chemical Engineering and Applied Chemistry, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (S.O.); (J.W.)
| | - Jiawei Wang
- Chemical Engineering and Applied Chemistry, Aston University, Aston Triangle, Birmingham B4 7ET, UK; (S.O.); (J.W.)
| | - Luyue Xia
- Zhejiang Province Key Laboratory of Biomass Fuel, Hangzhou 310014, China;
- College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jie Ren
- College of Biological, Chemical Science and Engineering, Jiaxing University, Jiaxing 314001, China;
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7
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Advanced Detection Techniques Using Artificial Intelligence in Processing of Berries. FOOD ENGINEERING REVIEWS 2021. [DOI: 10.1007/s12393-021-09298-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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The Impact of Optical Berry Sorting on Red Wine Composition and Sensory Properties. Foods 2021; 10:foods10020402. [PMID: 33673030 PMCID: PMC7917668 DOI: 10.3390/foods10020402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 11/16/2022] Open
Abstract
The impact of optical berry sorting was investigated using Grenache, Barbera, and Cabernet Sauvignon grapes from Yolo County, California in 2016. Optical sorting parameters were adjusted to remove underripe berries and material other than grapes using color parameters. Wines were made from three treatments, control (no sorting), sort (accepted material), and reject (material rejected by the optical sorter). The rate of rejection was approximately 14.9%, 3.9%, and 1.5% (w/w) for Grenache, Barbera, and Cabernet Sauvignon, respectively. Chemical composition in the finished wines was analyzed by the Adams-Harbertson assay and reversed-phase high-performance liquid chromatography for phenolics, and head-space solid-phase microextraction gas chromatography mass spectrometry for aroma profiling. In general, optical sorting was successful in removing underripe berries and material other than grapes as evidenced by lower ethanol levels and higher concentrations of total phenolics and tannin (due to the inclusion of material other than grapes) in wine made from rejected material. Despite this, no difference in final ethanol content and minimal differences in phenolic composition were observed between control and sort treatment wines for the three varieties studied. Differences were observed in the aroma profiles of the reject treatments for all three varieties compared to sort and control; however, few compounds differed significantly between the sort and control treatments. Descriptive sensory analysis revealed that panelists had difficulty distinguishing aroma, taste, mouthfeel, and color parameters among wines made from different treatments for all three varieties. Thus, optical sorting had minimal impact on wine sensory properties using the varieties and vineyards studied. Optical sorting may be used to differentiate and sort for different ripeness levels using color as a primary criterion; however, the impact on the resulting wine is likely dependent on the initial variability in grape ripeness.
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Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113769] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The soluble solids content (SSC) affects the flavor of green plums and is an important parameter during processing. In recent years, the hyperspectral technology has been widely used in the nondestructive testing of fruit ingredients. However, the prediction accuracy of most models can hardly be improved further. The rapid development of deep learning technology has established the foundation for the improvement of building models. A new hyperspectral imaging system aimed at measuring the green plum SSC is developed, and a sparse autoencoder (SAE)–partial least squares regression (PLSR) model is combined to further improve the accuracy of component prediction. The results of the experiment show that the SAE–PLSR model, which has a correlation coefficient of 0.938 and root mean square error of 0.654 for the prediction set, can achieve better performance for the SSC prediction of green plums than the three traditional methods. In this paper, integration approaches have combined three different pretreatment methods with PLSR to predict the SSC in green plums. The SAE–PLSR model has shown good prediction performance, indicating that the proposed SAE–PLSR model can effectively detect the SSC in green plums.
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Wang A, Sheng R, Li H, Agyekum AA, Hassan MM, Chen Q. Development of near‐infrared online grading device for long jujube. J FOOD PROCESS ENG 2020. [DOI: 10.1111/jfpe.13411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ancheng Wang
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Ren Sheng
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Huanhuan Li
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | | | - Md Mehedi Hassan
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
| | - Quansheng Chen
- School of Food and Biological EngineeringJiangsu University Zhenjiang China
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A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy. SENSORS 2020; 20:s20061586. [PMID: 32178312 PMCID: PMC7146621 DOI: 10.3390/s20061586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/25/2022]
Abstract
Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.
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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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