1
|
Qi H, Luo J, Chen G, Zhang J, Chen F, Li H, Shen C, Zhang C. Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024; 104:4309-4319. [PMID: 38305465 DOI: 10.1002/jsfa.13316] [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: 11/08/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
BACKGROUND Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. RESULTS This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. CONCLUSION The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.
Collapse
Affiliation(s)
- Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Jiahao Luo
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Gang Chen
- Zhejiang Dekfeller Intelligent Machinery Manufacturing Co., Ltd, Hangzhou, China
| | - Jianyi Zhang
- Zhejiang Dekfeller Intelligent Machinery Manufacturing Co., Ltd, Hangzhou, China
| | - Fengnong Chen
- School of Automation, School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China
| | - Hongyang Li
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cong Shen
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| |
Collapse
|
2
|
Ouyang H, Tang L, Ma J, Pang T. Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears. PLANTS (BASEL, SWITZERLAND) 2024; 13:1163. [PMID: 38674571 PMCID: PMC11055027 DOI: 10.3390/plants13081163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/12/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024]
Abstract
Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG-GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG-SVR model. The SG-CARS-GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG-CARS-GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.
Collapse
Affiliation(s)
- Hongkun Ouyang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (L.T.); (J.M.)
| | | | | | - Tao Pang
- College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625000, China; (L.T.); (J.M.)
| |
Collapse
|
3
|
Tang T, Luo Q, Yang L, Gao C, Ling C, Wu W. Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology. Foods 2023; 13:25. [PMID: 38201054 PMCID: PMC10778318 DOI: 10.3390/foods13010025] [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: 11/22/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.
Collapse
Affiliation(s)
- Ting Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Qing Luo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Liu Yang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Changlun Gao
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Caijin Ling
- Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Weibin Wu
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| |
Collapse
|
4
|
Li B, Han Z, Wang Q, Yang A, Liu Y. Detection of bruised loquats based on reflectance, absorbance and Kubelka–Munk spectra. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
|
5
|
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
| |
Collapse
|
6
|
Li J, He L, Liu M, Chen J, Xue L. Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm. Front Nutr 2022; 9:993737. [PMID: 36337614 PMCID: PMC9626814 DOI: 10.3389/fnut.2022.993737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
Canker is a common disease of navel oranges that is visible before harvest, and penicilliosis is a common disease occurring after harvest and storage. In this research, the typical fruit surface, canker spots, penicillium spore, and hypha of navel oranges were, respectively, identified by hyperspectral imaging. First, the light intensity on the edge of samples in hyperspectral images was improved by spherical correction. Then, independent component images and weight coefficients were obtained using independent component analysis. This approach, combined with use of a genetic algorithm, was used to select six characteristic wavelengths. The method achieved dimension reduction of hyperspectral data, and the testing time was reduced from 46.21 to 1.26 s for a self-developed online detection system. Finally, a deep learning neural network model was established, and the four kinds of surface pixels were identified accurately.
Collapse
Affiliation(s)
- Jing Li
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Liang He
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Muhua Liu
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Jinyin Chen
- Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang, China
| | - Long Xue
- Jiangxi Key Laboratory of Modern Agricultural Equipment, College of Engineering, Jiangxi Agricultural University, Nanchang, China
- Key Laboratory of Optics-Electrics Application of Biomaterials of Jiangxi Province, Jiangxi Agricultural University, Nanchang, China
- *Correspondence: Long Xue,
| |
Collapse
|
7
|
Study on Qualitative Impact Damage of Loquats Using Hyperspectral Technology Coupled with Texture Features. Foods 2022; 11:foods11162444. [PMID: 36010443 PMCID: PMC9407320 DOI: 10.3390/foods11162444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/26/2022] [Accepted: 08/11/2022] [Indexed: 11/17/2022] Open
Abstract
Bruising is one of the main problems in the post-harvest grading and processing of ‘Zaozhong 6’ loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis–NIR, 400–1000 nm), the visible region (Vis, 400–780 nm), and the near-infrared region (NIR, 781–1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit.
Collapse
|
8
|
Kim SY, Hong SJ, Kim E, Lee CH, Kim G. Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
|
9
|
Yu S, Fan J, Lu X, Wen W, Shao S, Guo X, Zhao C. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce. FRONTIERS IN PLANT SCIENCE 2022; 13:927832. [PMID: 35845657 PMCID: PMC9279906 DOI: 10.3389/fpls.2022.927832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction ( R p 2 ) of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher R p 2 than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.
Collapse
Affiliation(s)
- Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Song Shao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| |
Collapse
|
10
|
Navarro PJ, Miller L, Díaz-Galián MV, Gila-Navarro A, Aguila DJ, Egea-Cortines M. A novel ground truth multispectral image dataset with weight, anthocyanins, and Brix index measures of grape berries tested for its utility in machine learning pipelines. Gigascience 2022; 11:giac052. [PMID: 35701377 PMCID: PMC9197681 DOI: 10.1093/gigascience/giac052] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/11/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The combination of computer vision devices such as multispectral cameras coupled with artificial intelligence has provided a major leap forward in image-based analysis of biological processes. Supervised artificial intelligence algorithms require large ground truth image datasets for model training, which allows to validate or refute research hypotheses and to carry out comparisons between models. However, public datasets of images are scarce and ground truth images are surprisingly few considering the numbers required for training algorithms. RESULTS We created a dataset of 1,283 multidimensional arrays, using berries from five different grape varieties. Each array has 37 images of wavelengths between 488.38 and 952.76 nm obtained from single berries. Coupled to each multispectral image, we added a dataset with measurements including, weight, anthocyanin content, and Brix index for each independent grape. Thus, the images have paired measures, creating a ground truth dataset. We tested the dataset with 2 neural network algorithms: multilayer perceptron (MLP) and 3-dimensional convolutional neural network (3D-CNN). A perfect (100% accuracy) classification model was fit with either the MLP or 3D-CNN algorithms. CONCLUSIONS This is the first public dataset of grape ground truth multispectral images. Associated with each multispectral image, there are measures of the weight, anthocyanins, and Brix index. The dataset should be useful to develop deep learning algorithms for classification, dimensionality reduction, regression, and prediction analysis.
Collapse
Affiliation(s)
- Pedro J Navarro
- Escuela Técnica Superior de Ingeniería de Telecomunicación (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Leanne Miller
- Escuela Técnica Superior de Ingeniería de Telecomunicación (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - María Victoria Díaz-Galián
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Alberto Gila-Navarro
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Diego J Aguila
- Sociedad Cooperativa Las Cabezuelas, 30840 Alhama de Murcia, Spain
| | - Marcos Egea-Cortines
- Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Plaza del Hospital s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| |
Collapse
|
11
|
Cho BH, Kim YH, Lee KB, Hong YK, Kim KC. Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity. SENSORS 2022; 22:s22124378. [PMID: 35746159 PMCID: PMC9227650 DOI: 10.3390/s22124378] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 02/01/2023]
Abstract
It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.
Collapse
|
12
|
Wu X, Li G, He F. Nondestructive Analysis of Internal Quality in Pears with a Self-Made Near-Infrared Spectrum Detector Combined with Multivariate Data Processing. Foods 2021; 10:1315. [PMID: 34200438 PMCID: PMC8226885 DOI: 10.3390/foods10061315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/29/2021] [Accepted: 06/01/2021] [Indexed: 12/17/2022] Open
Abstract
The consumption of pears has increased, thanks not only to their delicious and juicy flavor, but also their rich nutritional value. Traditional methods of detecting internal qualities (e.g., soluble solid content (SSC), titratable acidity (TA), and taste index (TI)) of pears are reliable, but they are destructive, time-consuming, and polluting. It is necessary to detect internal qualities of pears rapidly and nondestructively by using near-infrared (NIR) spectroscopy. In this study, we used a self-made NIR spectrum detector with an improved variable selection algorithm, named the variable stability and cluster analysis algorithm (VSCAA), to establish a partial least squares regression (PLSR) model to detect SSC content in snow pears. VSCAA is a variable selection method based on the combination of variable stability and cluster analysis to select the infrared spectrum variables. To reflect the advantages of VSCAA, we compared the classical variable selection methods (synergy interval partial least squares (SiPLS), genetic algorithm (GA), successive projections algorithm (SPA), and bootstrapping soft shrinkage (BOSS)) to extract useful wavelengths. The PLSR model, based on the useful variables selected by SiPLS-VSCAA, was optimal for measuring SSC in pears, and the correlation coefficient of calibration (Rc), root mean square error of cross validation (RMSECV), correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.942, 0.198%, 0.936, 0.222%, and 2.857, respectively. Then, we applied these variable selection methods to select the characteristic wavelengths for measuring the TA content and TI value in snow pears. The prediction PLSR models, based on the variables selected by GA-BOSS to measure TA and that by GA-VSCAA to detect TI, were the best models, and the Rc, RMSECV, Rp and RPD were 0.931, 0.124%, 0.912, 0.151%, and 2.434 and 0.968, 0.080%, 0.968, 0.089%, and 3.775, respectively. The results showed that the self-made NIR-spectrum detector based on a portable NIR spectrometer with multivariate data processing was a good tool for rapid and nondestructive analysis of internal quality in pears.
Collapse
Affiliation(s)
- Xin Wu
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
- Department of Electronics and Internet of Things, Chongqing College of Electronic Engineering, Chongqing 401331, China
| | - Guanglin Li
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
| | - Fengyun He
- Department of Agricultural Engineering, College of Engineering and Technology, Southwest University, Chongqing 400715, China; (X.W.); (F.H.)
| |
Collapse
|
13
|
Wang Z, Fan S, Wu J, Zhang C, Xu F, Yang X, Li J. Application of long-wave near infrared hyperspectral imaging for determination of moisture content of single maize seed. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 254:119666. [PMID: 33744703 DOI: 10.1016/j.saa.2021.119666] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/28/2021] [Accepted: 02/28/2021] [Indexed: 05/28/2023]
Abstract
Moisture content (MC) is one of the most important factors for assessment of seed quality. However, the accurate detection of MC in single seed is very difficult. In this study, single maize seed was used as research object. A long-wave near infrared (LWNIR) hyperspectral imaging system was developed for acquiring reflectance images of the embryo and endosperm side of maize seed in the spectral range of 930-2548 nm, and the mixed spectra were extracted from both side of maize seeds. Then, Full-spectrum models were established and compared based on different types of spectra. It showed that models established based on spectra of the embryo side and mixed spectra obtained better performance than the endosperm side. Next, a combination of competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) was proposed to select the most effective wavelengths from full-spectrum data. In order to explore the stableness of wavelength selection algorithm, these methods were used for 200 independent experiments based on embryo side and mixed spectra, respectively. Each selection result was used as input of partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) to build calibration models for determining the MC of single maize seed. Results indicated that the CARS-SPA-LS-SVM model established with mixed spectra was optimal for MC prediction in all models by considering the accuracy, stableness and complexity of models. The prediction accuracy of CARS-SPA-LS-SVM model is Rpre = 0.9311 ± 0.0094 and RMSEP = 1.2131 ± 0.0702 in 200 independent assessment. The overall study revealed that the long-wave near infrared hyperspectral imaging can be used to non-invasively and fast measure the MC in single maize seed and a robust and accurate model could be established based on CARS-SPA-LS-SVM method coupled with mixed spectral. These results can provide a useful reference for assessment of other internal quality attributes (such as starch content) of single maize seed.
Collapse
Affiliation(s)
- Zheli Wang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Shuxiang Fan
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Jingzhu Wu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Chi Zhang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Fengying Xu
- Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
| | - Jiangbo Li
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
| |
Collapse
|
14
|
Wang B, Sun J, Xia L, Liu J, Wang Z, Li P, Guo Y, Sun X. The Applications of Hyperspectral Imaging Technology for Agricultural Products Quality Analysis: A Review. FOOD REVIEWS INTERNATIONAL 2021. [DOI: 10.1080/87559129.2021.1929297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Bao Wang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Jianfei Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Lianming Xia
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Junjie Liu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Zhenhe Wang
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Pei Li
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Yemin Guo
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| | - Xia Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong Province, P.R. China
- Shandong Provincial Engineering Research Center of Vegeable Safety and Quality Traceability, No.12 Zhangzhou Road, Zibo 255049, Shandong Province, PR China
- Zibo City Key Laboratory of Agricultural Product Safety Traceability, Zibo, China
| |
Collapse
|
15
|
Wu X, Li G, Liu X, He F. Rapid non‐destructive analysis of lignin using NIR spectroscopy and chemo‐metrics. Food Energy Secur 2021. [DOI: 10.1002/fes3.289] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Affiliation(s)
- Xin Wu
- College of Engineering and Technology Southwest University Chongqing China
- Chongqing College of Electronic Engineering Chongqing China
| | - Guanglin Li
- College of Engineering and Technology Southwest University Chongqing China
| | - Xuwen Liu
- College of Engineering and Technology Southwest University Chongqing China
| | - Fengyun He
- College of Engineering and Technology Southwest University Chongqing China
| |
Collapse
|
16
|
Yuan D, Jiang J, Qiao X, Qi X, Wang W. An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
17
|
Weng S, Yu S, Guo B, Tang P, Liang D. Non-Destructive Detection of Strawberry Quality Using Multi-Features of Hyperspectral Imaging and Multivariate Methods. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3074. [PMID: 32485900 PMCID: PMC7308843 DOI: 10.3390/s20113074] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Revised: 05/26/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023]
Abstract
Soluble solid content (SSC), pH, and vitamin C (VC) are considered as key parameters for strawberry quality. Spectral, color, and textural features from hyperspectral reflectance imaging of 400-1000 nm was to develop the non-destructive detection approaches for SSC, pH, and VC of strawberries by integrating various multivariate methods as partial least-squares regression (PLSR), support vector regression, and locally weighted regression (LWR). SSC, pH, and VC of 120 strawberries were statistically analyzed to facilitate the partitioning of data sets, which helped optimize the model. PLSR, with spectral and color features, obtained the optimal prediction of SSC with determination coefficient of prediction (Rp2) of 0.9370 and the root mean square error of prediction (RMSEP) of 0.1145. Through spectral features, the best prediction for pH was obtained by LWR with Rp2 = 0.8493 and RMSEP = 0.0501. Combination of spectral and textural features with PLSR provided the best results of VC with Rp2 = 0.8769 and RMSEP = 0.0279. Competitive adaptive reweighted sampling and uninformative variable elimination (UVE) were used to select important variables from the above features. Based on the important variables, the accuracy of SSC, pH, and VC prediction both gain the promotion. Finally, the distribution maps of SSC, pH, and VC over time were generated, and the change trend of three quality parameters was observed. Thus, the proposed method can nondestructively and accurately determine SSC, pH, and VC of strawberries and is expected to design and construct the simple sensors for the above quality parameters of strawberries.
Collapse
|
18
|
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
| |
Collapse
|
19
|
Fang Q, Wu R, Hu G, Lai A, Wu K, Zhang L, Feng J, Cao H. Dissipation behavior, residue distribution and risk assessment of three fungicides in pears. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:1757-1763. [PMID: 31825523 DOI: 10.1002/jsfa.10199] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 11/12/2019] [Accepted: 12/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Fungicides are often applied to pears before they are kept in storage facilities. The scientific application of pesticides can reduce unnecessary exposure, which in turn could benefit both humans and the environment. RESULTS We investigated dissipation behavior and residue distribution, and conducted risk assessments for prochloraz, pyraclostrobin, and tebuconazole in pears stored under different conditions using ultra-performance liquid chromatography (UPLC). The recoveries of the three fungicides ranged from 76.5% to 114.5%, and the coefficients of variation were 1.0%-8.5%. The half-life (t1/2 ) ranges for degradation of the three fungicides in Dangshan Su pear peel were 8.8-13.9 days after storage at 25 °C and 99.0-346.6 days after storage at 2 °C. Among the three fungicides, tebuconazole had the lowest residue concentration in pear pulp (maximum of 0.226 mg·kg-1 ) and the longest half-life (≥ 231.0 days). Accordingly, among these fungicides, tebuconazole is the most suitable for the preservation of Dangshan Su pears during storage. Finally, we analyzed samples of six pear varieties from markets in China and found that the residue concentrations of the three fungicides in pear pulp and fruit met Chinese standards. CONCLUSION The results provide a scientific basis for rationalizing the use of prochloraz, pyraclostrobin, and tebuconazole, and improving the safety of pears for eating. © 2019 Society of Chemical Industry.
Collapse
Affiliation(s)
- Qingkui Fang
- Provincial Key Laboratory for Agri-Food Safety, School of Plant Protection, Anhui Agricultural University, Hefei, China
- Key Laboratory of Biology and Sustainable Management of Plant Diseases and Pests of Anhui Higher Education Institutes, Anhui Agricultural University, Hefei, China
| | - Ruifeng Wu
- Provincial Key Laboratory for Agri-Food Safety, School of Resource & Environment, Anhui Agricultural University, Hefei, China
- Key Laboratory of Biology and Sustainable Management of Plant Diseases and Pests of Anhui Higher Education Institutes, Anhui Agricultural University, Hefei, China
| | - Guixian Hu
- Institute of Quality and Standards for Agricultural Products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Aiping Lai
- Institute of Quality and Standards for Agricultural Products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Kaixin Wu
- Provincial Key Laboratory for Agri-Food Safety, School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Linwei Zhang
- Provincial Key Laboratory for Agri-Food Safety, School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Jiajun Feng
- Provincial Key Laboratory for Agri-Food Safety, School of Plant Protection, Anhui Agricultural University, Hefei, China
| | - Haiqun Cao
- Provincial Key Laboratory for Agri-Food Safety, School of Plant Protection, Anhui Agricultural University, Hefei, China
- Key Laboratory of Biology and Sustainable Management of Plant Diseases and Pests of Anhui Higher Education Institutes, Anhui Agricultural University, Hefei, China
| |
Collapse
|
20
|
Khoshnoudi-Nia S, Moosavi-Nasab M. Prediction of various freshness indicators in fish fillets by one multispectral imaging system. Sci Rep 2019; 9:14704. [PMID: 31605023 PMCID: PMC6789145 DOI: 10.1038/s41598-019-51264-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 09/29/2019] [Indexed: 01/16/2023] Open
Abstract
In current study, a simple multispectral imaging (430–1010 nm) system along with linear and non-linear regressions were used to assess the various fish spoilage indicators during 12 days storage at 4 ± 2 °C. The indicators included Total-Volatile Basic Nitrogen (TVB-N) and Psychrotrophic Plate Count (PPC) and sensory score in fish fillets. immediately, after hyperspectral imaging, the reference values (TVB-N, PPC and sensory score) of samples were obtained by traditional method. To simplify the calibration models, nine optimal wavelengths were selected by genetic algorithm. The prediction performance of various chemometric models including partial least-squares regression (PLSR), multiple-linear regression (MLR), least-squares support vector machine (LS-SVM) and back-propagation artificial neural network (BP-ANN) were compared. All models showed acceptable performance for simultaneous predicting of PPC, TVB-N and sensory score (R2P ≥ 0.853 and RPD ≥ 2.603). Non-linear models were considered better quantitative model to predict all of three freshness indicators in fish fillets. Among the three spoilage indices, the best predictive power was obtained for PPC value and the weakest one was acquired for TVB-N content prediction. The best model for prediction TVB-N (R2p = 0.862; RMSEP = 3.542 and RPD = 2.678) and sensory score (R2p = 0.912; RMSEP = 1.802 and RPD = 3.33) belonged to GA-LS-SVM and for prediction of PPC value was BP-ANN (R2p = 0.921; RMSEP = 0.504 and RPD = 3.64). Therefore, developing multispectral imaging system based on LS-SVM model seems to be suitable for simultaneous prediction of all three indicators (R2P > 0.862 and RPD > 2.678). Further studies needed to improve the accuracy and applicability of HSI system for predicting freshness of rainbow-trout fish.
Collapse
Affiliation(s)
- Sara Khoshnoudi-Nia
- Seafood Processing Research Group, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
| | - Marzieh Moosavi-Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of Agriculture, Shiraz University, PO Box: 71441-65186, Shiraz, Iran.
| |
Collapse
|
21
|
Song J, Li G, Yang X. Optimizing genetic algorithm-partial least squares model of soluble solids content in Fukumoto navel orange based on visible-near-infrared transmittance spectroscopy using discrete wavelet transform. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2019; 99:4898-4903. [PMID: 30924947 DOI: 10.1002/jsfa.9717] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 02/24/2019] [Accepted: 03/26/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND The thick rind of Fukumoto navel orange is a great barrier to light penetration, which makes it difficult to evaluate the internal quality of Fukumoto navel orange accurately by visible-near-infrared (Vis-NIR) transmittance spectroscopy. The information carried by the transmission spectrum is limited. Thus, the application of genetic algorithm (GA) for variable selection may not reach the expected results, and selected variables may contain redundancy. In this paper, we present the use of discrete wavelet transforms for optimizing a GA-partial least squares (PLS) model based on Vis-NIR transmission spectra of Fukumoto navel orange. Haar, Db, Sym, Coif and Bior wavelets were used to compress the spectral data selected by GA. Then a PLS model was established based on the variables compressed by each wavelet function. RESULTS The use of Db4, Sym4, Coif2 and Bior3.5 succeeded in further simplification of the GA-PLS model by reducing the number of variables by 40-44% without decreasing the prediction accuracy. The application of Bior3.5 not only could reduce the number of variables in the GA-PLS model by 40%, but also increase the value of correlation coefficient of prediction by 1% and decrease the value of root mean square error of prediction by 3%. CONCLUSIONS The results indicated that the combination of GA and discrete wavelet transforms for variable selection in the internal quality assessment of Fukumoto navel orange by Vis-NIR transmittance spectroscopy was feasible. © 2019 Society of Chemical Industry.
Collapse
Affiliation(s)
- Jie Song
- Key Laboratory of Hilly and Mountain Areas of Chongqing, College of Engineering and Technology, Southwest University, Chongqing, China
| | - Guanglin Li
- Key Laboratory of Hilly and Mountain Areas of Chongqing, College of Engineering and Technology, Southwest University, Chongqing, China
| | - Xiaodong Yang
- Key Laboratory of Hilly and Mountain Areas of Chongqing, College of Engineering and Technology, Southwest University, Chongqing, China
| |
Collapse
|
22
|
Liu Y, Wang Q, Gao X, Xie A. Total phenolic content prediction in
Flos Lonicerae
using hyperspectral imaging combined with wavelengths selection methods. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.13224] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yunhong Liu
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Qingqing Wang
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Xiuwei Gao
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| | - Anguo Xie
- School of Food and Bio‐engineeringHenan University of Science and Technology Luoyang China
| |
Collapse
|
23
|
Wang Q, Liu Y, Gao X, Xie A, Yu H. Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2019. [DOI: 10.1007/s11694-019-00180-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
|
24
|
Khoshnoudi‐Nia S, Moosavi‐Nasab M. Comparison of various chemometric analysis for rapid prediction of thiobarbituric acid reactive substances in rainbow trout fillets by hyperspectral imaging technique. Food Sci Nutr 2019; 7:1875-1883. [PMID: 31139402 PMCID: PMC6526668 DOI: 10.1002/fsn3.1043] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 03/13/2019] [Accepted: 03/27/2019] [Indexed: 12/31/2022] Open
Abstract
This study explores the potential application of hyperspectral imaging (HSI; 430-1,010 nm) coupled with different linear and nonlinear models for rapid nondestructive evaluation of thiobarbituric acid-reactive substances (TBARS) value in rainbow trout (Oncorhynchus mykiss) fillets during 12 days of cold storage (4 ± 2°C). HSI data and TBARS value of fillets were obtained in the laboratory. The primary prediction models were established based on linear partial least squares regression (PLSR) and least squares support vector machine (LS-SVM). In full spectral range, the prediction capability of LS-SVM ( R P 2 = 0.829; RMSEP = 0.128 mg malondialdehyde [MDA]/kg) was better than PLSR ( R P 2 = 0.748; RMSEP = 0.155 mg MDA/kg) model and LS-SVM model exhibited satisfactory prediction performance ( R P 2 > 0.82). To simplify the calibration models, a combination of uninformative variable elimination and backward regression (UB) was used as variable selection. Nine wavelengths were selected. Various chemometric analysis methods including linear PLSR and multiple linear regression and nonlinear LS-SVM and back-propagation artificial neural network (BP-ANN) were compared. The simplified models showed better capability than those were built based on the whole dataset in prediction of TBARS values. Moreover, the nonlinear models were preferred over linear models. Among the four chemometric algorithms, the best and weakest models were LS-SVM and PLSR model, respectively. UB-LS-SVM model was the optimal models for predicting TBARS value in rainbow trout fillets ( R P 2 = 0.831; RMSEP = 0.125 mg MDA/kg). The establishing of lipid-oxidation prediction model in rainbow trout fish was complicated, due to the fluctuations of TBARS values during storage. Therefore, further researches are needed to improve the prediction results and applicability of HIS technique for prediction of TBARS value in rainbow trout fish.
Collapse
Affiliation(s)
- Sara Khoshnoudi‐Nia
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
| | - Marzieh Moosavi‐Nasab
- Seafood Processing Research Group & Department of Food Science and Technology, School of AgricultureShiraz UniversityShirazIran
| |
Collapse
|
25
|
Ahn D, Choi JY, Kim HC, Cho JS, Moon KD, Park T. Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks. SENSORS 2019; 19:s19071560. [PMID: 30935139 PMCID: PMC6480044 DOI: 10.3390/s19071560] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 03/20/2019] [Accepted: 03/28/2019] [Indexed: 11/16/2022]
Abstract
There is an increasing demand for acquiring details of food nutrients especially among those who are sensitive to food intakes and weight changes. To meet this need, we propose a new approach based on deep learning that precisely estimates the composition of carbohydrates, proteins, and fats from hyperspectral signals of foods obtained by using low-cost spectrometers. Specifically, we develop a system consisting of multiple deep neural networks for estimating food nutrients followed by detecting and discarding estimation anomalies. Our comprehensive performance evaluation demonstrates that the proposed system can maximize estimation accuracy by automatically identifying wrong estimations. As such, if consolidated with the capability of reinforcement learning, it will likely be positioned as a promising means for personalized healthcare in terms of food safety.
Collapse
Affiliation(s)
- DaeHan Ahn
- Department of Robotics Engineering, Hanyang University, 55 Hanyang daehak-ro, Ansan 15588, Korea.
| | - Ji-Young Choi
- School of Food Science and Biotechnology, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea.
| | - Hee-Chul Kim
- School of Food Science and Biotechnology, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea.
| | - Jeong-Seok Cho
- Food and Bio-industry Research Institute, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea.
| | - Kwang-Deog Moon
- School of Food Science and Biotechnology, Kyungpook National University, 80 daehak-ro, bukgu, Daegu 41566, Korea.
| | - Taejoon Park
- Department of Robotics Engineering, Hanyang University, 55 Hanyang daehak-ro, Ansan 15588, Korea.
| |
Collapse
|
26
|
Yang B, Guo W, Li W, Li Q, Liu D, Zhu X. Portable, visual, and nondestructive detector integrating Vis/NIR spectrometer for sugar content of kiwifruits. J FOOD PROCESS ENG 2019. [DOI: 10.1111/jfpe.12982] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Biao Yang
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
- Key Laboratory of Agricultural Internet of ThingsMinistry of Agriculture and Rural Affairs Yangling Shaanxi China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling Shaanxi China
| | - Weiqiang Li
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Qianqian Li
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Dayang Liu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| | - Xinhua Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University Yangling Shaanxi China
| |
Collapse
|