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Xu S, Guo Y, Liang X, Lu H. Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review. Foods 2024; 13:1116. [PMID: 38611420 PMCID: PMC11012010 DOI: 10.3390/foods13071116] [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: 02/22/2024] [Revised: 04/01/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.
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Affiliation(s)
- Sai Xu
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
| | - Yinghua Guo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Xin Liang
- Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China;
- College of Engineering, South China Agricultural University, Guangzhou 510642, China;
| | - Huazhong Lu
- Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
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Xu S, Lu H, Liang X, Ference C, Qiu G, Fan C. Modeling and De-Noising for Nondestructive Detection of Total Soluble Solid Content of Pomelo by Using Visible/Near Infrared Spectroscopy. Foods 2023; 12:2966. [PMID: 37569235 PMCID: PMC10418712 DOI: 10.3390/foods12152966] [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: 05/22/2023] [Revised: 07/03/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
The flavor of Pomelo is highly variable and difficult to determine without peeling the fruit. The quality of pomelo flavor is due largely to the total soluble solid content (TSSC) in the fruit and there is a commercial need for a quick but nondestructive TSSC detection method for the industrial grading of pomelo. Due to the large size and thick mesocarp of pomelo, determining the internal quality of a pomelo fruit in a nondestructive manner is difficult, and the detection accuracy is further complicated by the noise typically generated by the common methods for the internal quality detection of other fruits. Thus, the aim of this study was to determine the optimal method to accurately detect pomelo TSSC and find a de-noising model which reduces the influence of noise on the optimal method's results. After developing a full-transmission visible/near infrared (VIS/NIR) spectroscopy sampling method, the confirming experimental results showed that the optimal pomelo TSSC detection model was Savitzky Golay + standard normal variate + competitive adaptive reweighted sampling + partial least squares regression. The R2 and RMSE of the calibration set for pomelo TSSC detection were 0.8097 and 0.8508, respectively, and the R2 and RMSE of the validation set for pomelo TSSC detection were 0.8053 and 0.8888, respectively. Both reference and dark de-noising are important for pomelo internal quality detection and should be calibrated frequently to compensate for time drift. This study found that large sensor response translation noise can be reduced with an artificial horizontal shift. Data supplementation is efficient for improving the adaption of the detection model for batch differences in pomelo samples. Using this optimized de-noising model to compensate for time drift, sensor response translation, and batch differences, the developed detection method is capable of satisfying the requirements of the industry (TSSC detection R2 was equal or larger than 0.9, RMSE was less than 1). These results indicate that full-transmission VIS/NIR spectroscopy can be exploited to realize the nondestructive detection of pomelo TSSC on an industrial scale, and that the methodologies used in this study can be immediately implemented in real-world production.
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Affiliation(s)
- Sai Xu
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; (S.X.); (X.L.); (G.Q.); (C.F.)
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China
| | - Huazhong Lu
- Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Xin Liang
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; (S.X.); (X.L.); (G.Q.); (C.F.)
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China
| | - Christopher Ference
- USDA, Agricultural Research Service, US Pacific Basin Agricultural Research Center, 64 Nowelo Street, Hilo, HI 96720, USA;
| | - Guangjun Qiu
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; (S.X.); (X.L.); (G.Q.); (C.F.)
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510640, China
| | - Changxiang Fan
- Institute of Facility Agriculture of Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; (S.X.); (X.L.); (G.Q.); (C.F.)
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Ansah FA, Amo-Boateng M, Siabi EK, Bordoh PK. Location of seed spoilage in mango fruit using X-ray imaging and convolutional neural networks. SCIENTIFIC AFRICAN 2023. [DOI: 10.1016/j.sciaf.2023.e01649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
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Wang Y, Zhang Y, Yuan Y, Zhao Y, Nie J, Nan T, Huang L, Yang J. Nutrient content prediction and geographical origin identification of red raspberry fruits by combining hyperspectral imaging with chemometrics. Front Nutr 2022; 9:980095. [PMID: 36386936 PMCID: PMC9642070 DOI: 10.3389/fnut.2022.980095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/30/2022] [Indexed: 09/13/2024] Open
Abstract
The geographical origin and the important nutrient contents greatly affect the quality of red raspberry (RRB, Rubus idaeus L.), a popular fruit with various health benefits. In this study, a chemometrics-assisted hyperspectral imaging (HSI) method was developed for predicting the nutrient contents, including pectin polysaccharides (PPS), reducing sugars (RS), total flavonoids (TF) and total phenolics (TP), and identifying the geographical origin of RRB fruits. The results showed that these nutrient contents in RRB fruits had significant differences between regions (P < 0.05) and could be well predicted based on the HSI full or effective wavelengths selected through competitive adaptive reweighted sampling (CARS) and variable iterative space shrinkage approach (VISSA). The best prediction results of PPS, RS, TF, and TP contents were achieved with the highest residual predictive deviation (RPD) values of 3.66, 3.95, 2.85, and 4.85, respectively. The RRB fruits from multi-regions in China were effectively distinguished by using the first derivative-partial least squares discriminant analysis (DER-PLSDA) model, with an accuracy of above 97%. Meanwhile, the fruits from three protected geographical indication (PGI) regions were successfully classified by using the orthogonal partial least squares discrimination analysis (OPLSDA) model, with an accuracy of above 98%. The study results indicate that HSI assisted with chemometrics is a promising method for predicting the important nutrient contents and identifying the geographical origin of red raspberry fruits.
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Affiliation(s)
- Youyou Wang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yue Zhang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- School of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuwei Yuan
- Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou, China
| | - Yuyang Zhao
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jing Nie
- Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences; Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou, China
| | - Tiegui Nan
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Luqi Huang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jian Yang
- State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
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Wang J, Huo Y, Wang Y, Zhao H, Li K, Liu L, Shi Y. Grading detection of “Red Fuji” apple in Luochuan based on machine vision and near-infrared spectroscopy. PLoS One 2022; 17:e0271352. [PMID: 35925926 PMCID: PMC9352003 DOI: 10.1371/journal.pone.0271352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022] Open
Abstract
A quality detection system for the “Red Fuji” apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and the cross-sectional diameter Wp in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple’s comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient Rp of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify “Red Fuji” apple in Luochuan.
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Affiliation(s)
- Jin Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Yujia Huo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Yutong Wang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Haoyu Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Kai Li
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
| | - Li Liu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
- * E-mail:
| | - Yinggang Shi
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
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