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An T, Fan Y, Tian X, Wang Q, Wang Z, Fan S, Huang W. Green analytical assay for the viability assessment of single maize seeds using double-threshold strategy for catalase activity and malondialdehyde content. Food Chem 2024; 455:139889. [PMID: 38833865 DOI: 10.1016/j.foodchem.2024.139889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/06/2024]
Abstract
The development of nondestructive technology for the detection of seed viability is challenging. In this study, to establish a green and effective method for the viability assessment of single maize seeds, a two-stage seed viability detection method was proposed. The catalase (CAT) activity and malondialdehyde (MDA) content were selected as the most key biochemical components affecting maize seed viability, and regression prediction models were developed based on their hyperspectral information and a data fusion strategy. Qualitative discrimination models for seed viability evaluation were constructed based on the predicted response values of the selected key biochemical components. The results showed that the double components thresholds strategy achieved the highest discrimination accuracy (92.9%), providing a crucial approach for the rapid and environmentally friendly detection of seed viability.
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Affiliation(s)
- Ting An
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Engineering and Technology, Southwest University, Chongqing 400715, China
| | - Yaoyao Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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2
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Zhang F, Wang M, Zhang F, Xiong Y, Wang X, Ali S, Zhang Y, Fu S. Hyperspectral imaging combined with GA-SVM for maize variety identification. Food Sci Nutr 2024; 12:3177-3187. [PMID: 38726456 PMCID: PMC11077206 DOI: 10.1002/fsn3.3984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 05/12/2024] Open
Abstract
The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.
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Affiliation(s)
- Fu Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
- Collaborative Innovation Center of Advanced Manufacturing for Machinery and Equipment of Henan ProvinceLuoyangChina
| | - Mengyao Wang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Fangyuan Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Ying Xiong
- College of Agriculture/PeonHenan University of Science and TechnologyLuoyangChina
| | - Xinyue Wang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Shaukat Ali
- Wah Engineering College, University of WahWah CanttPakistan
| | - Yakun Zhang
- College of Agricultural Equipment EngineeringHenan University of Science and TechnologyLuoyangChina
| | - Sanling Fu
- School of Physical EngineeringHenan University of Science and TechnologyLuoyangChina
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3
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Zhang T, Lu L, Song Y, Yang M, Li J, Yuan J, Lin Y, Shi X, Li M, Yuan X, Zhang Z, Zeng R, Song Y, Gu L. Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques. FRONTIERS IN PLANT SCIENCE 2024; 14:1342970. [PMID: 38288409 PMCID: PMC10822997 DOI: 10.3389/fpls.2023.1342970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024]
Abstract
The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.
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Affiliation(s)
- Tingting Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Long Lu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yihu Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Minyu Yang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jing Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Jiduan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Yuquan Lin
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Xingren Shi
- Huzhou Wuxing Jinnong Ecological Agriculture Development Co., Ltd, Huzhou, China
| | - Mingjie Li
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Xiaotan Yuan
- Pharmaceutical Development Board of Zherong County, Ningde, China
| | - Zhongyi Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Rensen Zeng
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Yuanyuan Song
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Li Gu
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Key Laboratory of Biological Breeding for Fujian and Taiwan Crops, Ministry of Agriculture and Rural Affairs, Fujian Agriculture and Forestry University, Fuzhou, China
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4
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Xia Y, Liu W, Meng J, Hu J, Liu W, Kang J, Luo B, Zhang H, Tang W. Principles, developments, and applications of spatially resolved spectroscopy in agriculture: a review. FRONTIERS IN PLANT SCIENCE 2024; 14:1324881. [PMID: 38269139 PMCID: PMC10805836 DOI: 10.3389/fpls.2023.1324881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/19/2023] [Indexed: 01/26/2024]
Abstract
Agriculture is the primary source of human survival, which provides the most basic living and survival conditions for human beings. As living standards continue to improve, people are also paying more attention to the quality and safety of agricultural products. Therefore, the detection of agricultural product quality is very necessary. In the past decades, the spectroscopy technique has been widely used because of its excellent results in agricultural quality detection. However, traditional spectral inspection methods cannot accurately describe the internal information of agricultural products. With the continuous research and development of optical properties, it has been found that the internal quality of an object can be better reflected by separating the properties of light, such as its absorption and scattering properties. In recent years, spatially resolved spectroscopy has been increasingly used in the field of agricultural product inspection due to its simple compositional structure, low-value cost, ease of operation, efficient detection speed, and outstanding ability to obtain information about agricultural products at different depths. It can also separate optical properties based on the transmission equation of optics, which allows for more accurate detection of the internal quality of agricultural products. This review focuses on the principles of spatially resolved spectroscopy, detection equipment, analytical methods, and specific applications in agricultural quality detection. Additionally, the optical properties methods and direct analysis methods of spatially resolved spectroscopy analysis methods are also reported in this paper.
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Affiliation(s)
- Yu Xia
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Wenxi Liu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jingwu Meng
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Jinghao Hu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Wenbo Liu
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Jie Kang
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
| | - Bin Luo
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Han Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wei Tang
- School of Electrical and Control Engineering, Shaanxi University of Science & Technology, Xi’an, Shaanxi, China
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Mei M, Cai Z, Zhang X, Sun C, Zhang J, Peng H, Li J, Shi R, Zhang W. Early bruising detection of 'Korla' pears by low-cost visible-LED structured-illumination reflectance imaging and feature-based classification models. FRONTIERS IN PLANT SCIENCE 2023; 14:1324152. [PMID: 38034568 PMCID: PMC10687182 DOI: 10.3389/fpls.2023.1324152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
Introduction Nondestructive detection of thin-skinned fruit bruising is one of the main challenges in the automated grading of post-harvest fruit. The structured-illumination reflectance imaging (SIRI) is an emerging optical technique with the potential for detection of bruises. Methods This study presented the pioneering application of low-cost visible-LED SIRI for detecting early subcutaneous bruises in 'Korla' pears. Three types of bruising degrees (mild, moderate and severe) and ten sets of spatial frequencies (50, 100, 150, 200, 250, 300, 350, 400, 450 and 500 cycles m-1) were analyzed. By evaluation of contrast index (CI) values, 150 cycles m-1 was determined as the optimal spatial frequency. The sinusoidal pattern images were demodulated to get the DC, AC, and RT images without any stripe information. Based on AC and RT images, texture features were extracted and the LS-SVM, PLS-DA and KNN classification models combined the optimized features were developed for the detection of 'Korla' pears with varying degrees of bruising. Results and discussion It was found that RT images consistently outperformed AC images regardless of type of model, and LS-SVM model exhibited the highest detection accuracy and stability. Across mild, moderate, severe and mixed bruises, the LS-SVM model with RT images achieved classification accuracies of 98.6%, 98.9%, 98.5%, and 98.8%, respectively. This study showed that visible-LED SIRI technique could effectively detect early bruising of 'Korla' pears, providing a valuable reference for using low-cost visible LED SIRI to detect fruit damage.
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Affiliation(s)
- Mengwen Mei
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Zhonglei Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Xinran Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Chanjun Sun
- Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment, Jiangsu University, Zhenjiang, China
| | - Junyi Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Huijie Peng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China
- Engineering Research Center for Production Mechanization of OasisCharacteristic Cash Crop, Ministry of Education, Shihezi, China
| | - Jiangbo Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Ruiyao Shi
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wei Zhang
- Department of Computer Technology and Science, Anhui University of Finance and Economics, Bengbu, China
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6
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Tian X, Liu X, He X, Zhang C, Li J, Huang W. Detection of early bruises on apples using hyperspectral reflectance imaging coupled with optimal wavelengths selection and improved watershed segmentation algorithm. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:6689-6705. [PMID: 37267465 DOI: 10.1002/jsfa.12764] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/26/2023] [Accepted: 06/03/2023] [Indexed: 06/04/2023]
Abstract
BACKGROUND Bruises caused by mechanical collision during the harvesting and storage and transportation period are difficult to detect using traditional machine vision technologies because there is no obvious difference in appearance between bruised and sound tissues. As a result of its fast and non-destructive characteristics, hyperspectral imaging technology is a potential tool for non-destructive detection of fruit surface defects. RESULTS In the present study, visible near infrared hyperspectral reflectance images of healthy apples and bruised apples at 6, 12 and 24 h were obtained. To reduce hyperspectral data dimension, optimal wavelength selection algorithms including principal component analysis (PCA) and band ratio methods were utilized to select the effective wavelengths and enhance the contrast between bruised and sound tissues. Then pseudo-color image transformation technology combining with improved watershed segmentation algorithm (IWSA) were employed to recognize the bruise spots. The result obtained showed that band ratio images obtained better detection performance than that of PCA. The G component derived from pseudo-color image ofλ 821 - λ 752 / λ 821 + λ 752 followed by IWSA obtained the best segmentation performance for bruise spots. Finally, a multispectral imaging system for the detection of bruised apple was developed to verify the effectiveness of the proposed two-band ratio algorithm, obtaining recognition rates of 93.3%, 92.2% and 92.5% for healthy, bruised and overall apples, respectively. CONCLUSION The bruise detection algorithm proposed in the present study has potential to detect bruised apple in online practical applications and hyperspectral reflectance imaging offers a useful reference for the detection of surficial defects of fruit. © 2023 Society of Chemical Industry.
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Affiliation(s)
- Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xuefeng Liu
- Chongqing Three Gorges Academy of Agricultural Sciences, Chongqing, China
| | - Xin He
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiangbo Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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Fan Y, An T, Wang Q, Yang G, Huang W, Wang Z, Zhao C, Tian X. Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network. FRONTIERS IN PLANT SCIENCE 2023; 14:1248598. [PMID: 37711294 PMCID: PMC10497746 DOI: 10.3389/fpls.2023.1248598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/11/2023] [Indexed: 09/16/2023]
Abstract
The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.
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Affiliation(s)
- Yaoyao Fan
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ting An
- 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
| | - Guang Yang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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Liu Y, Zhou S, Wan Z, Qiu Z, Zhao L, Pang K, Li C, Yin Z. A Self-Supervised Anomaly Detector of Fruits Based on Hyperspectral Imaging. Foods 2023; 12:2669. [PMID: 37509761 PMCID: PMC10378827 DOI: 10.3390/foods12142669] [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: 06/17/2023] [Revised: 07/07/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral-spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a 'spectral-spatial' feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective 'spectral-spatial' latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process.
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Affiliation(s)
- Yisen Liu
- Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Songbin Zhou
- Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Zhiyong Wan
- Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Zefan Qiu
- Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Lulu Zhao
- Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China
| | - Kunkun Pang
- Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China
| | - Chang Li
- Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China
| | - Zexuan Yin
- Guangdong Key Laboratory of Modern Control Technology, Guangzhou 510070, China
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Ghanei Ghooshkhaneh N, Mollazade K. Optical Techniques for Fungal Disease Detection in Citrus Fruit: A Review. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03005-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
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11
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The impact of high-quality data on the assessment results of visible/near-infrared hyperspectral imaging and development direction in the food fields: a review. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01822-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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12
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Sun Y, Wang X, Pan L, Hu Y. Influence of maturity on bruise detection of peach by structured multispectral imaging. Curr Res Food Sci 2023; 6:100476. [PMID: 36941891 PMCID: PMC10023935 DOI: 10.1016/j.crfs.2023.100476] [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: 11/25/2022] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Peaches are easily bruising during all stages of postharvest handling, maturity can affect the characteristics and detection of bruising, which is directly related to the quality and shelf life of peach. The main objective of this research was to investigate the effect of maturity on the early detection of postharvest bruising in peach based on structured multispectral imaging (S-MSI) system. The S-MSI data was measured for bruised peaches, followed by microstructural (CLSM), and biochemical (oxidative browning-related enzyme activities, gene expression, and phenolic compound metabolism) measurements. As the maturity increases, the external impact stress could further induce the accumulation of phenolics through the phenylpropane pathway and pulp oxidative browning, resulting in more pronounced external damage; and the spectral reflectance value of bruised peach was getting smaller, and the spectral waveform gradually flattened out. Three characteristic bands of 781, 824, 867 nm were selected from structured spectra (669-955 nm) related to bruising. The watershed algorithm was adopted for bruise detection, the detection rates for bruised peaches based on three maturity levels (S1-S3) were 91-92%, 90.71-97.43%, and 97.14-99.86%, respectively. This research demonstrated that S-MSI system coupled with watershed algorithm, can enhance our capability of detecting the early bruised peaches of different maturity levels.
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Affiliation(s)
- Ye Sun
- College of Food Science and Light Industry, Nanjing Technology University, Nanjing, 211816, China
- College of Engineering, Nanjing Agricultural University, 210031, Nanjing, China
| | - Xiaochan Wang
- College of Engineering, Nanjing Agricultural University, 210031, Nanjing, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yonghong Hu
- College of Food Science and Light Industry, Nanjing Technology University, Nanjing, 211816, China
- Corresponding author. 30 Puzhu South Road, College of Food Science and Light Industry, Nanjing Technology University, 211816, Nanjing, China.
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Yin H, Li B, Liu YD, Zhang F, Su CT, Ou-Yang AG. Detection of early bruises on loquat using hyperspectral imaging technology coupled with band ratio and improved Otsu method. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 283:121775. [PMID: 36007346 DOI: 10.1016/j.saa.2022.121775] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/18/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
The bruising is one of the major factors affecting the quality of loquat and the bruised areas of loquat are also prone to harbor bacteria and molds. Therefore, it is critical to detect early bruises of loquat. In this study, a method based on hyperspectral imaging technology coupled with band ratio and improved Otsu method was proposed to detect early bruises of loquat. Firstly, the principal component cluster analysis was used to analyze the three regions of Vis-NIR (397.5-1014.0 nm), Vis (397.5-780.0 nm), and NIR (780.0-1014.0 nm), respectively. It was found that the Vis-NIR and NIR spectral regions along PC1 could be used to effectively distinguish bruised tissues. Then, the key wavelength images corresponding to the two regions were selected according to the load curve, respectively, and two sets of PC images and band ratio images of them were established. After comparison, it was found that the band ratio image Q651.3 / 904.3 was the most suitable for subsequent analysis of detecting early bruises of loquat. Finally, in order to evaluate the segmentation effect of the improved Otsu method, the segmentation results of the global threshold and the Otsu method were compared with it, respectively, and it was found that the performance of the improved Otsu method was best. However, since the stem-end area and the bruised area have similar intensity features causing mis-segmentation, the stem-end area was removed by curvature-assisted Hough transform circle detection (CACD) algorithm. And all test set samples were used to evaluate the performance of the proposed method, and the overall accuracy of it was 96.0 %. The results show that the detection method proposed in this study has the potential to detect early bruises of loquat in online practical applications, and it provides a theoretical basis for hyperspectral imaging in the bruise detection of fruit.
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Affiliation(s)
- Hai Yin
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China
| | - Bin Li
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China
| | - Yan-de Liu
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China
| | - Feng Zhang
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China
| | - Cheng-Tao Su
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China
| | - Ai-Guo Ou-Yang
- Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China.
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14
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Chen J, Yang C, Yuan C, Li Y, An T, Dong C. Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy. Sci Rep 2022; 12:20721. [PMID: 36456868 PMCID: PMC9715558 DOI: 10.1038/s41598-022-25112-6] [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: 08/29/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (Rp) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.
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Affiliation(s)
- Jiayou Chen
- grid.495239.00000 0004 4657 1319Liming Vocational University, Quanzhou, 362007 China ,grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Chongshan Yang
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Changbo Yuan
- grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
| | - Yang Li
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China
| | - Ting An
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.263906.80000 0001 0362 4044College of Engineering and Technology, Southwest University, Chongqing, 400715 China
| | - Chunwang Dong
- grid.410727.70000 0001 0526 1937Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou, 310008 China ,grid.452757.60000 0004 0644 6150Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250033 China
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15
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Li B, Yin H, Liu YD, Zhang F, Yang AK, Su CT, Ou-yang AG. Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method. J Anal Sci Technol 2022. [DOI: 10.1186/s40543-022-00334-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractTo deduce the process of bruise and reduce the number of bruised fruits from the source, the storage time of yellow peaches after bruise should be identified. In order to distinguish the different storage times of mild bruise’s yellow peaches more effectively than current detection methods, the combined hyperspectral imaging and machine learning method was proposed. Firstly, the sample bruise region spectrum was extracted as spectral features, and then, the hyperspectral images were processed by Principal Component Analysis (PCA), and eight single-wavelength images were selected according to the weight coefficient curve of PC1 images, and the gray values of the selected images were calculated as image features. Finally, in order to find the optimal discriminative model, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were built based on spectral features, image features, and spectral features combined with image features, respectively. The results show that the XGBoost models based on spectral features, image features, and spectral features combined with image features are the optimal models with the overall accuracy of 77.50%, 87.50% and 90.00%, respectively. To simplify the model, Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to screen the wavelength of the normalized spectral data, and then they were fused with the image feature data again and the XGBoost model with an overall model accuracy of 95.00% was built. To sum up, the combined hyperspectral imaging and machine learning method can be used to distinguish the different storage times (2 h, 8 h, 24 h and 48 h) of mild bruise’s yellow peaches effectively. It provides a certain theoretical basis for hyperspectral imaging technology in fruit bruise detection.
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16
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Graphical Image Region Extraction with K-Means Clustering and Watershed. J Imaging 2022; 8:jimaging8060163. [PMID: 35735962 PMCID: PMC9224791 DOI: 10.3390/jimaging8060163] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/21/2022] [Accepted: 06/01/2022] [Indexed: 02/01/2023] Open
Abstract
With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.
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Wang Z, Huang W, Tian X, Long Y, Li L, Fan S. Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods. FRONTIERS IN PLANT SCIENCE 2022; 13:849495. [PMID: 35620676 PMCID: PMC9127793 DOI: 10.3389/fpls.2022.849495] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000-2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.
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Affiliation(s)
- Zheli Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Lianjie Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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18
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Pang Q, Huang W, Fan S, Zhou Q, Wang Z, Tian X. Detection of early bruises on apples using hyperspectral imaging combining with
YOLOv3
deep learning algorithm. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13952] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Qi Pang
- College of Information Shanghai Ocean University Shanghai China
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
| | - Wenqian Huang
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
| | - Shuxiang Fan
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
| | - Quan Zhou
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
| | - Zheli Wang
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
| | - Xi Tian
- Information Technology Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry Sciences Beijing China
- College of Information and Electrical Engineering China Agricultural University Beijing China
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19
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Zhang Y, Yang X, Cai Z, Fan S, Zhang H, Zhang Q, Li J. Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method. Foods 2021; 10:foods10122983. [PMID: 34945536 PMCID: PMC8700705 DOI: 10.3390/foods10122983] [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: 10/13/2021] [Revised: 11/13/2021] [Accepted: 11/30/2021] [Indexed: 11/24/2022] Open
Abstract
Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.
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Affiliation(s)
- Yifei Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Correspondence: (X.Y.); (J.L.)
| | - Zhonglei Cai
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
| | - Haiyun Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
| | - Jiangbo Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; (Y.Z.); (Z.C.); (H.Z.); (Q.Z.)
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
- Correspondence: (X.Y.); (J.L.)
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