1
|
Xu Y, Yang X, Zhang J, Zhou X, Luo L, Zhang Q. Visual analysis of sea buckthorn fruit moisture content based on deep image processing technology. Food Chem 2024; 453:139558. [PMID: 38781892 DOI: 10.1016/j.foodchem.2024.139558] [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: 02/06/2024] [Revised: 04/11/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024]
Abstract
The effect of moisture content changes during drying processing on the appearance of sea buckthorn was studied. Using computer vision methods and various image processing methods to collect and analyze images during the drying process of sea buckthorn fruit. Sea buckthorn is dried in a drying oven at a temperature of 65 °C and Level 1 wind speed conditions. The images of the entire drying process of sea buckthorn fruit were collected at 30-min intervals. Deep mining and transformation of image information through various image processing methods. By calibrating and modeling the color components, real-time online detection of the moisture content of sea buckthorn fruit can be achieved. After modeling, this article attempted to use LSTM (Long Short Term Memory) to predict the appearance of sea buckthorn fruit with supercritical moisture content. Different agricultural products adapt to different color spaces, but after standard modeling with a certain amount of data, applying color components to detect moisture content is a very good method.
Collapse
Affiliation(s)
- Yu Xu
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Xuhai Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China; Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832000, China; Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832000, China
| | - Junyi Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Xiang Zhou
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Liwei Luo
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
| | - Qian Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China; Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi 832000, China; Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832000, China.
| |
Collapse
|
2
|
Li W, Wang Q, Wang Y. Non-Destructive Inspection of Physicochemical Indicators of Lettuce at Rosette Stage Based on Visible/Near-Infrared Spectroscopy. Foods 2024; 13:1863. [PMID: 38928805 PMCID: PMC11202870 DOI: 10.3390/foods13121863] [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: 05/18/2024] [Revised: 06/06/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024] Open
Abstract
Lettuce is a globally important cash crop, valued by consumers for its nutritional content and pleasant taste. However, there is limited research on the changes in the growth indicators of lettuce during its growth period in domestic settings. Quality assessment primarily relies on subjective evaluations, resulting in significant variability. This study focused on hydroponically grown lettuce during the rosette stage and investigated the patterns of changes in the indicators and spectral curves over time. By employing spectral preprocessing and selecting characteristic wavelengths, three models were developed to predict the indicators. The results showed that the optimal model structures were S_G-UVE-PLSR (SSC and vitamin C) and Nor-CARS-PLSR (moisture content). The PLSR models achieved prediction set correlation coefficients of 0.8648, 0.8578, and 0.8047, with residual prediction deviations of 1.9685, 1.9568, and 1.6689, respectively. The optimal models were integrated into a portable device, using real-time analysis software written in Matlab2021a, for the prediction of the physicochemical indicators of lettuce during the rosette stage. The results demonstrated prediction set correlation coefficients of 0.8215, 0.8472, and 0.7671, with root mean square errors of prediction of 0.5348, 1.5813, and 2.3347 for a sample size of 180. The small discrepancies between the predicted and actual values indicate that the developed device can meet the requirements for real-time detection.
Collapse
Affiliation(s)
- Wei Li
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
| | - Qiaohua Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
- Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
| | - Yingli Wang
- College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; (W.L.); (Y.W.)
| |
Collapse
|
3
|
Qiu Z, Li G, Huang Z, He X, Zhang Z, Li Z, Du H. Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling. FRONTIERS IN PLANT SCIENCE 2024; 15:1354290. [PMID: 38872886 PMCID: PMC11169883 DOI: 10.3389/fpls.2024.1354290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 06/15/2024]
Abstract
Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.
Collapse
Affiliation(s)
- Zhichao Qiu
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Gangao Li
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Zongbao Huang
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Xiuhan He
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Zilin Zhang
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Zhiwei Li
- College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong, China
- College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong, China
| | - Huiling Du
- Department of Basic Sciences, Shanxi Agricultural University, Jinzhong, China
| |
Collapse
|
4
|
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] [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.
Collapse
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.
| |
Collapse
|
5
|
Zhang J, Wu X, He C, Wu B, Zhang S, Sun J. Near-Infrared Spectroscopy Combined with Fuzzy Improved Direct Linear Discriminant Analysis for Nondestructive Discrimination of Chrysanthemum Tea Varieties. Foods 2024; 13:1439. [PMID: 38790739 PMCID: PMC11119828 DOI: 10.3390/foods13101439] [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: 04/12/2024] [Revised: 05/02/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024] Open
Abstract
The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties.
Collapse
Affiliation(s)
- Jiawei Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shuyu Zhang
- Mengxi Honors College, Jiangsu University, Zhenjiang 212013, China; (J.Z.); (S.Z.)
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (C.H.); (J.S.)
| |
Collapse
|
6
|
Zhu D, Han J, Liu C, Zhang J, Qi Y. Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging. FRONTIERS IN PLANT SCIENCE 2024; 15:1344143. [PMID: 38410736 PMCID: PMC10895056 DOI: 10.3389/fpls.2024.1344143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/24/2024] [Indexed: 02/28/2024]
Abstract
Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed's nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the R p 2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.
Collapse
Affiliation(s)
- Dongyu Zhu
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Junying Han
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Chengzhong Liu
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China
| | - Jianping Zhang
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
| | - Yanni Qi
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, China
| |
Collapse
|
7
|
Zhang S, Yin Y, Liu C, Li J, Sun X, Wu J. Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123050. [PMID: 37379715 DOI: 10.1016/j.saa.2023.123050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/14/2023] [Accepted: 06/18/2023] [Indexed: 06/30/2023]
Abstract
Rapid detection of wheat flour grade played an important role in the food industry. In this work, hyperspectral technology was used to detect five types of wheat flour. An analysis model was established based on the reflectance of samples at 968 ∼ 2576 nm. Moreover, multivariate scattering correction (MSC), standard normalized variate (SNV), and Savitzky-Golay (S-G) convolution smoothing were used for preprocessing, which was employed to reduce the influence of noise in the original spectrum. In order to simplify the model, competing adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uninformative variable elimination (UVE) and the UVE-CARS algorithm were applied to extract feature wavelengths. Both partial least squares discriminant analysis (PLS-DA) model and support vector machine (SVM) model were established according to feature wavelengths. Furthermore, particle swarm optimization (PSO) algorithm was adopted to optimize the search of SVM model parameters, such as the penalty coefficient c and the regularization coefficient g. Experimental results suggested that the non-linear discriminant model for wheat flour grades was better than the linear discriminant model. It was considered that the MSC-UVE-CARS-PSO-SVM model achieved the best forecasting results for wheat flour grade discrimination, with 100% accuracy both in the calibration set and the validation set. It further shows that the classification of wheat flour grade can be effectively realized by using the hyperspectral and SVM discriminant analysis model, which proves the potential of hyperspectral reflectance technology in the qualitative analysis of wheat flour grade.
Collapse
Affiliation(s)
- Shanzhe Zhang
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Yingqian Yin
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
| | - Cuiling Liu
- Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
| | - Jiacong Li
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Xiaorong Sun
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| | - Jingzhu Wu
- Key Laboratory of Industry Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
| |
Collapse
|
8
|
Qi H, Huang Z, Sun Z, Tang Q, Zhao G, Zhu X, Zhang C. Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1283921. [PMID: 37936942 PMCID: PMC10627025 DOI: 10.3389/fpls.2023.1283921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 10/09/2023] [Indexed: 11/09/2023]
Abstract
Vigor is one of the important factors that affects rice yield and quality. Rapid and accurate detection of rice seed vigor is of great importance for rice production. In this study, near-infrared hyperspectral imaging technique and transfer learning were combined to detect rice seed vigor. Four varieties of artificial-aged rice seeds (Yongyou12, Yongyou1540, Suxiangjing100, and Longjingyou1212) were studied. Different convolutional neural network (CNN) models were built to detect the vigor of the rice seeds. Two transfer strategies, fine-tuning and MixStyle, were used to transfer knowledge among different rice varieties for vigor detection. The experimental results showed that the convolutional neural network model of Yongyou12 classified the vigor of Yongyou1540, Suxiangjing100, and Longjingyou1212 through MixStyle transfer knowledge, and the accuracy reached 90.00%, 80.33%, and 85.00% in validation sets, respectively, which was better or close to the initial modeling performances of each variety. MixStyle statistics are based on probabilistic mixed instance-level features of cross-source domain training samples. When training instances, new domains can be synthesized, which increases the domain diversity of the source domain, thereby improving the generalization ability of the trained model. This study would help rapid and accurate detection of a large varieties of crop seeds.
Collapse
Affiliation(s)
- Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Zihong Huang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Zeyu Sun
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Qizhe Tang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Guangwu Zhao
- College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin’an, China
| | - Xuhua Zhu
- Smart Agriculture Research Institute, Zhejiang Top Cloud-agri Technology Co., Ltd., Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| |
Collapse
|
9
|
Huo J, Zhang M, Wang D, S Mujumdar A, Bhandari B, Zhang L. New preservation and detection technologies for edible mushrooms: A review. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2023; 103:3230-3248. [PMID: 36700618 DOI: 10.1002/jsfa.12472] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/11/2022] [Accepted: 01/26/2023] [Indexed: 06/17/2023]
Abstract
Edible mushrooms are nutritious, tasty, and have medicinal value, which makes them very popular. Fresh mushrooms have a high water content and a crisp texture. They demonstrate strong metabolic activity after harvesting. However, they are prone to textural changes, microbial infestation, and nutritional and flavor loss, and they therefore require appropriate post-harvest processing and preservation. Important factors affecting safety and quality during their processing and storage include their quality, source, microbial contamination, physical damage, and chemical residues. Thus, these aspects should be tested carefully to ensure safety. In recent years, many new techniques have been used to preserve mushrooms, including electrofluidic drying and cold plasma treatment, as well as new packaging and coating technologies. In terms of detection, many new detection techniques, such as nuclear magnetic resonance (NMR), imaging technology, and spectroscopy can be used as rapid and effective means of detection. This paper reviews the new technological methods for processing and detecting the quality of mainstream edible mushrooms. It mainly introduces their working principles and application, and highlights the future direction of preservation, processing, and quality detection technologies for edible mushrooms. Adopting appropriate post-harvest processing and preservation techniques can maintain the organoleptic properties, nutrition, and flavor of mushrooms effectively. The use of rapid, accurate, and non-destructive testing methods can provide a strong assurance of food safety. At present, these new processing, preservation and testing methods have achieved good results but at the same time there are certain shortcomings. So it is recommended that they also be continuously researched and improved, for example through the use of new technologies and combinations of different technologies. © 2023 Society of Chemical Industry.
Collapse
Affiliation(s)
- Jingyi Huo
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Min Zhang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- Jiangsu Province International Joint Laboratory on Fresh Food Smart Processing and Quality Monitoring, Jiangnan University, Wuxi, China
| | - Dayuan Wang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, China
- China General Chamber of Commerce Key Laboratory on Fresh Food Processing & Preservation, Jiangnan University, Wuxi, China
| | - Arun S Mujumdar
- Department of Bioresource Engineering, Macdonald College, McGill University, Quebec, Canada
| | - Bhesh Bhandari
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, Australia
| | - Lujun Zhang
- R&D Center, Shandong Qihe Biotechnology Co., Ltd, Zibo, China
| |
Collapse
|
10
|
He HJ, Chen Y, Li G, Wang Y, Ou X, Guo J. Hyperspectral imaging combined with chemometrics for rapid detection of talcum powder adulterated in wheat flour. Food Control 2023. [DOI: 10.1016/j.foodcont.2022.109378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
11
|
Non-Destructive Hyperspectral Imaging for Rapid Determination of Catalase Activity and Ageing Visualization of Wheat Stored for Different Durations. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248648. [PMID: 36557781 PMCID: PMC9785524 DOI: 10.3390/molecules27248648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
(1) In order to accurately judge the new maturity of wheat and better serve the collection, storage, processing and utilization of wheat, it is urgent to explore a fast, convenient and non-destructively technology. (2) Methods: Catalase activity (CAT) is an important index to evaluate the ageing of wheat. In this study, hyperspectral imaging technology (850-1700 nm) combined with a BP neural network (BPNN) and a support vector machine (SVM) were used to establish a quantitative prediction model for the CAT of wheat with the classification of the ageing of wheat based on different storage durations. (3) Results: The results showed that the model of 1ST-SVM based on the full-band spectral data had the best prediction performance (R2 = 0.9689). The SPA extracted eleven characteristic bands as the optimal wavelengths, and the established model of MSC-SPA-SVM showed the best prediction result with R2 = 0.9664. (4) Conclusions: The model of MSC-SPA-SVM was used to visualize the CAT distribution of wheat ageing. In conclusion, hyperspectral imaging technology can be used to determine the CAT content and evaluate wheat ageing, rapidly and non-destructively.
Collapse
|
12
|
An C, Yang K, Zhu J, Guo W, Lu C, Zhu X. Qualitative identification of mature milk adulteration in bovine colostrum using noise-reduced dielectric spectra and linear model. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:7313-7322. [PMID: 35763549 DOI: 10.1002/jsfa.12097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The rapid and accurate identification of colostrum, a strong non-homogeneous food, remains a challenge. In the present study, the dielectric spectra including the dielectric constant (ε') and loss factor (ε″) of 154 colostrum samples adulterated with 0-50% mature milk were measured from 20 to 4500 MHz. RESULTS The results showed that the noise-reducing spectral preprocessing, including Savitzky-Golay (S-G), second derivative (SD), and S-G + SD, was significantly better than scattering-eliminating, including standard normal variate (SNV), multiplicative scatter correction (MSC), and SNV + MSC. The combination of S-G and SD was the best. Principal component analysis results demonstrated that dielectric spectroscopy is less susceptible to the inhomogeneity of colostrum and can be used to identify doped colostrum. The identification performance of linear models was better than that of non-linear models. The established linear discriminant analysis model based on full spectra had the best accuracy rates of 99.14% and 97.37% in the calibration and validation sets, respectively. Confirmatory tests on samples from different sources confirmed the satisfactory robustness of the proposed model. CONCLUSION We found that the main unfavorable effect on the identification based on dielectric spectroscopy was noise interference, rather than scattering effect caused by inhomogeneity of colostrum. The satisfactory results undoubtedly cast light on rapid detection of strongly non-homogeneous foods based on dielectric spectroscopy. © 2022 Society of Chemical Industry.
Collapse
Affiliation(s)
- Changqing An
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
| | - Ke Yang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
| | - Jieliang Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
| | - Chang Lu
- Guangzhou Institute of Industrial Technology, Guangzhou, China
| | - Xinhua Zhu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Shaanxi Research Center of Agricultural Equipment Engineering Technology, Yangling, China
| |
Collapse
|
13
|
Zhang C, Huang W, Liang X, He X, Tian X, Chen L, Wang Q. Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding. FRONTIERS IN PLANT SCIENCE 2022; 13:956636. [PMID: 36186064 PMCID: PMC9520625 DOI: 10.3389/fpls.2022.956636] [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: 05/31/2022] [Accepted: 07/28/2022] [Indexed: 06/16/2023]
Abstract
Slight crack of cottonseed is a critical factor influencing the germination rate of cotton due to foamed acid or water entering cottonseed through testa. However, it is very difficult to detect cottonseed with slight crack using common non-destructive detection methods, such as machine vision, optical spectroscopy, and thermal imaging, because slight crack has little effect on morphology, chemical substances or temperature. By contrast, the acoustic method shows a sensitivity to fine structure defects and demonstrates potential application in seed detection. This paper presents a novel method to detect slightly cracked cottonseed using air-coupled ultrasound with a light-weight vision transformer (ViT) and a sound-to-image encoding method. The echo signal of air-coupled ultrasound from cottonseed is obtained by non-contact and non-destructive methods. The intrinsic mode functions (IMFs) of ultrasound signal are obtained as the sound features using variational mode decomposition (VMD) approach. Then the sound features are converted into colorful images by a color encoding method. This method uses different colored lines to represent the changes of different values of IMFs according to the specified encoding period. A light-weight MobileViT method is utilized to identify the slightly cracked cottonseeds using encoding colorful images corresponding to cottonseeds. The experimental results show an average overall recognition accuracy of 90.7% for slightly cracked cottonseed from normal cottonseed, which indicates that the proposed method is reliable to applications in detection task of cottonseed with slight crack.
Collapse
Affiliation(s)
- Chi Zhang
- 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
| | - Xiaoting Liang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- College of Information Technology, Shanghai Ocean University, Shanghai, China
| | - Xin He
- 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
| | - Liping Chen
- 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
| |
Collapse
|
14
|
Li Q, Wu X, Zheng J, Wu B, Jian H, Sun C, Tang Y. Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms. Foods 2022; 11:foods11142101. [PMID: 35885343 PMCID: PMC9323386 DOI: 10.3390/foods11142101] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/09/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.
Collapse
Affiliation(s)
- Qiulin Li
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China; (Q.L.); (C.S.); (Y.T.)
| | - Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China;
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Jun Zheng
- Department of Electrical and Control Engineering, Research Institute of Zhejiang University-Taizhou, Taizhou 318000, China
- Correspondence:
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China;
| | - Hao Jian
- China Railway Construction Electrification Bureau Group Co., Ltd., Beijing 100020, China;
| | - Changzhi Sun
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China; (Q.L.); (C.S.); (Y.T.)
| | - Yibiao Tang
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China; (Q.L.); (C.S.); (Y.T.)
| |
Collapse
|
15
|
Nansen C, Imtiaz MS, Mesgaran MB, Lee H. Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects. PLANT METHODS 2022; 18:74. [PMID: 35658997 PMCID: PMC9164469 DOI: 10.1186/s13007-022-00912-z] [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: 12/14/2021] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.
Collapse
Affiliation(s)
- Christian Nansen
- Department of Entomology and Nematology, University of California, Davis, USA.
- Department of Entomology and Nematology, UC Davis Briggs Hall, Room 367, Davis, CA, 95616, USA.
| | - Mohammad S Imtiaz
- Department of Electrical & Computer Engineering, Bradley University, Peoria, USA
| | | | - Hyoseok Lee
- Department of Entomology and Nematology, University of California, Davis, USA
| |
Collapse
|
16
|
An D, Zhang L, Liu Z, Liu J, Wei Y. Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality. Crit Rev Food Sci Nutr 2022; 63:9766-9796. [PMID: 35442834 DOI: 10.1080/10408398.2022.2066062] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.
Collapse
Affiliation(s)
- Dong An
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Liu Zhang
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhe Liu
- College of Land Science and Technology, China Agricultural University, Beijing, China
| | - Jincun Liu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Yaoguang Wei
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| |
Collapse
|
17
|
Liu Q, Wang Z, Long Y, Zhang C, Fan S, Huang W. Variety classification of coated maize seeds based on Raman hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 270:120772. [PMID: 34973616 DOI: 10.1016/j.saa.2021.120772] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/18/2021] [Accepted: 12/13/2021] [Indexed: 05/27/2023]
Abstract
As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.
Collapse
Affiliation(s)
- Qingyun Liu
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Zuchao Wang
- School of Science, China University of Geosciences (Beijing), Beijing 100083, China
| | - Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
| |
Collapse
|
18
|
Panda BK, Mishra G, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2021.110889] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
19
|
Mishra G, Panda BK, Ramirez WA, Jung H, Singh CB, Lee SH, Lee I. Application of SWIR hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of Aflatoxin B1 in single kernel almonds. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2021.112954] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
20
|
Tugnolo A, Giovenzana V, Malegori C, Oliveri P, Casson A, Curatitoli M, Guidetti R, Beghi R. A reliable tool based on near-infrared spectroscopy for the monitoring of moisture content in roasted and ground coffee: A comparative study with thermogravimetric analysis. Food Control 2021. [DOI: 10.1016/j.foodcont.2021.108312] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
21
|
Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112110319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper presents an extended comparison study between 16 different linear and non-linear regression methods to predict the sugar, pH, and anthocyanin contents of grapes through hyperspectral imaging (HIS). Despite the numerous studies on this subject that can be found in the literature, they often rely on the application of one or a very limited set of predictive methods. The literature on multivariate regression methods is quite extensive, so the analytical domain explored is too narrow to guarantee that the best solution has been found. Therefore, we developed an integrated linear and non-linear predictive analytics comparison framework (L&NL-PAC), fully integrated with five preprocessing techniques and five different classes of regression methods, for an effective and robust comparison of all alternatives through a robust Monte Carlo double cross-validation stratified data splitting scheme. L&NLPAC allowed for the identification of the most promising preprocessing approaches, best regression methods, and wavelengths most contributing to explaining the variability of each enological parameter for the target dataset, providing important insights for the development of precision viticulture technology, based on the HSI of grape. Overall, the results suggest that the combination of the Savitzky−Golay first derivative and ridge regression can be a good choice for the prediction of the three enological parameters.
Collapse
|
22
|
Zhou Q, Huang W, Tian X, Yang Y, Liang D. Identification of the variety of maize seeds based on hyperspectral images coupled with convolutional neural networks and subregional voting. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2021; 101:4532-4542. [PMID: 33452811 DOI: 10.1002/jsfa.11095] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/08/2021] [Accepted: 01/16/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Maize is one of the most important food crops in the world. Many different varieties of maize seeds are similar in size and appearance, so distinguishing the varieties of maize seed is a significant research topic. This study used hyperspectral image processing coupled with convolutional neural network (CNN) and a subregional voting method to recognize different varieties of maize seed. RESULTS First, visible and near-infrared (NIR-visible) hyperspectral images were obtained. Savitzky-Golay (SG) smoothing and first derivative (FD) were used to pretreat the raw spectra and highlight the spectral differences of samples of different varieties. Second, the region of interest (ROI) of each sample was divided into several subregions according to the shape and the number of pixels. Then, a method was proposed for reshaping the images of pixel spectra for the CNN and the training model was established. Finally, using subregional voting, one prediction result was generated from the prediction results of several original subregions in one sample. The results showed that, for six varieties of normal maize seeds, the tests identified embryoid and non-embryoid forms with 93.33% and 95.56% accuracy, respectively. For six varieties of sweet maize seeds, the test accuracy in embryoid and non-embryoid forms was 97.78% and 98.15%, respectively. CONCLUSION The maize seed was identified accurately. The present study demonstrated that the CNN model for spectral image coupled with subregional voting represents a new approach for the identification of varieties of maize seed. © 2021 Society of Chemical Industry.
Collapse
Affiliation(s)
- Quan Zhou
- School of Electronics and Information Engineering, Anhui University, Hefei, China
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Wenqian Huang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Xi Tian
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Yi Yang
- Beijing Research Center of Intelligent Equipment for Agriculture, Beijing, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing, China
| | - Dong Liang
- School of Electronics and Information Engineering, Anhui University, Hefei, China
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
| |
Collapse
|
23
|
Li H, Zhang L, Sun H, Rao Z, Ji H. Identification of soybean varieties based on hyperspectral imaging technology and one‐dimensional convolutional neural network. J FOOD PROCESS ENG 2021. [DOI: 10.1111/jfpe.13767] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Hao Li
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
| | - Liu Zhang
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
| | - Heng Sun
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
| | - Zhenhong Rao
- College of Science China Agricultural University Beijing China
| | - Haiyan Ji
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education China Agricultural University Beijing China
- Key Laboratory of Agricultural Information Acquisition Technology Ministry of Agriculture China Agricultural University Beijing China
| |
Collapse
|
24
|
Kusumiyati, Hadiwijaya Y, Putri IE, Munawar AA. Multi-product calibration model for soluble solids and water content quantification in Cucurbitaceae family, using visible/near-infrared spectroscopy. Heliyon 2021; 7:e07677. [PMID: 34401571 PMCID: PMC8353486 DOI: 10.1016/j.heliyon.2021.e07677] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022] Open
Abstract
Latest studies on Vis/NIR research mostly focused on particular products. Developing a model for a specific product is costly and laborious. This study utilized visible/near-infrared (Vis/NIR) spectroscopy to evaluate the quality attributes of six products of the Cucurbitaceae family, with a single estimation model, rather than individually. The study made use of six intact products, zucchini, bitter gourd, ridge gourd, melon, chayote, and cucumber. Subsequently, the multi-product models for soluble solids content (SSC) and water content were created using partial least squares regression (PLSR) method. The PLSR modeling produced satisfactory results, the coefficient of determination in calibration set (R2c) was discovered to be 0.95 and 0.92, while the root mean squares error of calibration (RMSEC) was found to be 0.41 and 0.61, for SSC and water content, respectively. These models were able to accurately predict the unknown samples with coefficient of determination in prediction set (R2p) of 0.96 and 0.92, as well as root mean squares error of prediction (RMSEP) of 0.32 and 0.58, while the ratio of prediction to deviation (RPD) was found to be 5.68 and 3.69 for SSC and water content, respectively. This shows Vis/NIR spectroscopy was able to quantify the SSC and water content of six products of Cucurbitaceae family, using a single model.
Collapse
Affiliation(s)
- Kusumiyati
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Yuda Hadiwijaya
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Ine Elisa Putri
- Department of Agronomy, Faculty of Agriculture, Universitas Padjadjaran, Sumedang 45363, Indonesia
| | - Agus Arip Munawar
- Department of Agricultural Engineering, Faculty of Agriculture, Universitas Syiah Kuala, Indonesia
| |
Collapse
|
25
|
Bao Y, Liu J, Zhong Y, Chen Y, Zhai D, Wang Q, Brennan CS, Liu H. Kernel partial least squares model for pectin content in peach using near‐infrared spectroscopy. Int J Food Sci Technol 2021. [DOI: 10.1111/ijfs.14817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Yao Bao
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Jianliang Liu
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
- Modern agriculture research center Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Yuming Zhong
- College of Environmental Science and Engineering Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Yumin Chen
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Dequan Zhai
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Qing Wang
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| | - Charles Stephen Brennan
- Department of Food, Wine and Molecular Biosciences University of Lincoln Christchurch85084New Zealand
| | - Huifan Liu
- College of Light Industry and Food Zhongkai University of Agriculture and Engineering Guangzhou Guangdong510225China
| |
Collapse
|
26
|
Cui H, Cheng Z, Li P, Miao A. Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4744. [PMID: 32842673 PMCID: PMC7506873 DOI: 10.3390/s20174744] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 08/16/2020] [Accepted: 08/20/2020] [Indexed: 11/17/2022]
Abstract
Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400-1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.
Collapse
Affiliation(s)
- Huawei Cui
- College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Zhishang Cheng
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| | - Peng Li
- Department of Electronic Engineering, School of Information, Yunnan University, Kunming 650091, China;
| | - Aimin Miao
- College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China;
| |
Collapse
|