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Yang T, Zheng X, Xiao H, Shan C, Zhang J. Moisture content online detection system based on multi-sensor fusion and convolutional neural network. FRONTIERS IN PLANT SCIENCE 2024; 15:1289783. [PMID: 38501134 PMCID: PMC10944943 DOI: 10.3389/fpls.2024.1289783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/15/2024] [Indexed: 03/20/2024]
Abstract
To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model's predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.
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Affiliation(s)
- Taoqing Yang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi, China
| | - Xia Zheng
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi, China
| | - Hongwei Xiao
- College of Engineering, China Agricultural University, Beijing, China
| | - Chunhui Shan
- College of Food, Shihezi University, Shihezi, China
| | - Jikai Zhang
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China
- Key Laboratory of Modern Agricultural Machinery Corps, Shihezi, China
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Abstract
Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.
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Chen X, Jiao Y, Liu B, Chao W, Duan X, Yue T. Using hyperspectral imaging technology for assessing internal quality parameters of persimmon fruits during the drying process. Food Chem 2022; 386:132774. [PMID: 35358859 DOI: 10.1016/j.foodchem.2022.132774] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 11/19/2022]
Abstract
The crucial features of persimmon are required to detect real-time moisture, water-soluble tannin, and soluble solids contents during the drying process. This study developed a method based on hyperspectral imaging (HSI) to execute online and non-destructive assaying of persimmon features. A total of 144 samples were collected, and 150 bands were scanned. The spectral data were analyzed by partial least squares regression (PLSR), principal component regression (PCR), least squares support vector regression (LS-SVR), and radial basis function neural network (RBFNN) with seven preprocessing methods. LS-SVR provided excellent performance for moisture content prediction, while PLSR was better in the analysis of water-soluble tannin and soluble solids contents. Successive projection algorithm (SPA) was used to select the optimal wavelengths to simplify the models, and about twenty important variables were chosen. Overall, these results indicate that HSI could be considered a valuable technique to quantify chemical constituents in dried persimmon fruits.
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Affiliation(s)
- Xiaoxi Chen
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China
| | - Yaling Jiao
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China
| | - Bin Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China; Fuping Modern Agriculture Comprehensive Demonstration Station, Northwest A&F University, Fuping, Shaanxi 711799, China.
| | - Wenhui Chao
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China
| | - Xuchang Duan
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China; Fuping Modern Agriculture Comprehensive Demonstration Station, Northwest A&F University, Fuping, Shaanxi 711799, China.
| | - Tianli Yue
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Laboratory of Quality and Safety Risk Assessment for Agro-Products, Ministry of Agriculture, Yangling, Shaanxi 712100, China; National Engineering Research Center of Agriculture Integration Test, Yangling, Shaanxi 712100, China
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Xu L, Wang X, Chen H, Xin B, He Y, Huang P. Predicting internal parameters of kiwifruit at different storage periods based on hyperspectral imaging technology. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01477-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Arefi A, Sturm B, von Gersdorff G, Nasirahmadi A, Hensel O. Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying. Lebensm Wiss Technol 2021. [DOI: 10.1016/j.lwt.2021.112297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Huang H, Hu X, Tian J, Jiang X, Luo H, Huang D. Rapid detection of the reducing sugar and amino acid nitrogen contents of Daqu based on hyperspectral imaging. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.103970] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Ren Y, Lin X, Lei T, Sun DW. Recent developments in vibrational spectral analyses for dynamically assessing and monitoring food dehydration processes. Crit Rev Food Sci Nutr 2021; 62:4267-4293. [PMID: 34275402 DOI: 10.1080/10408398.2021.1947773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Dehydration is one of the most widely used food processing techniques, which is sophisticated in nature. Rapid and accurate prediction of dehydration performance and its effects on product quality is still a difficult task. Traditional analytical methods for evaluating food dehydration processes are laborious, time-consuming and destructive, and they are not suitable for online applications. On the other hand, vibrational spectral techniques coupled with chemometrics have emerged as a rapid and noninvasive tool with excellent potential for online evaluation and control of the dehydration process to improve final dried food quality. In the current review, the fundamental of food dehydration and five types of vibrational spectral techniques, and spectral data processing methods are introduced. Critical overtones bands related to dehydration attributes in the near-infrared (NIR) region and the state-of-the-art applications of vibrational spectral analyses in evaluating food quality attributes as affected by dehydration processes are summarized. Research investigations since 2010 on using vibrational spectral technologies combined with chemometrics to continuously monitor food quality attributes during dehydration processes are also covered in this review.
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Affiliation(s)
- Yuqiao Ren
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Xiaohui Lin
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Tong Lei
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
| | - Da-Wen Sun
- Food Refrigeration and Computerized Food Technology (FRCFT), School of Biosystems and Food Engineering, Agriculture & Food Science Centre, University College Dublin (UCD), National University of Ireland, Belfield, Dublin 4, Ireland
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Taira S, Kiriake‐Yoshinaga A, Shikano H, Ikeda R, Kobayashi S, Yoshinaga K. Localization analysis of essential oils in perilla herb ( Perilla frutescens var. crispa) using derivatized mass spectrometry imaging. Food Sci Nutr 2021; 9:2779-2784. [PMID: 34026091 PMCID: PMC8116838 DOI: 10.1002/fsn3.2232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/24/2021] [Accepted: 02/27/2021] [Indexed: 11/10/2022] Open
Abstract
The localization of essential oils, including flavor components, in perilla herb (Perilla frutescens var. crispa) were visually determined using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) imaging. The surface of a perilla leaf was peeled using a cyanoacrylate adhesion compound and contained oil glands that retained their morphology and chemical properties. We imaged the three essential oils perillaldehyde, β-caryophyllene, and rosmarinic acid (RA). Perillaldehyde was derivatized using glycine to prevent evaporation and allow its detection and imaging while localized in oil glands. β-caryophyllene also localized in the oil glands and not in the epidermis region. RA was detected throughout the leaf, including the oil glands. Quantitative data for the three essential oils were obtained by gas chromatography- or liquid chromatography-MS. The concentrations of perillaldehyde, β-caryophyllene, and RA were 12.6 ± 0.62, 0.27 ± 0.02, and 0.16 ± 0.02 [mg/g] in the paste sample of perilla herb. Peeling using a cyanoacrylate adhesion compound, and derivatization of a target such as an aroma component have great potential for mass spectrometry imaging for multiple essential oils.
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Affiliation(s)
- Shu Taira
- Faculty of Food and Agricultural SciencesFukushima UniversityFukushimaJapan
| | | | - Hitomi Shikano
- Faculty of Food and Agricultural SciencesFukushima UniversityFukushimaJapan
| | - Ryuzoh Ikeda
- Faculty of Food and Agricultural SciencesFukushima UniversityFukushimaJapan
| | - Shoko Kobayashi
- Research Center for Food SafetyGraduate School of Agricultural and Life SciencesThe University of TokyoTokyoJapan
| | - Kazuaki Yoshinaga
- Faculty of Food and Agricultural SciencesFukushima UniversityFukushimaJapan
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