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Wang Z, Ding F, Ge Y, Wang M, Zuo C, Song J, Tu K, Lan W, Pan L. Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 316:124344. [PMID: 38688212 DOI: 10.1016/j.saa.2024.124344] [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: 12/28/2023] [Revised: 03/21/2024] [Accepted: 04/23/2024] [Indexed: 05/02/2024]
Abstract
In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.
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Affiliation(s)
- Zhenjie Wang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Fangchen Ding
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Yan Ge
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Mengyao Wang
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Changzhou Zuo
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Jin Song
- College of Artificial Intelligence, Nanjing Agricultural University, No. 40, Dianjiangtai Road, Nanjing, Jiangsu 210095, China
| | - Kang Tu
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Weijie Lan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
| | - Leiqing Pan
- College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; Sanya Institute of Nanjing Agricultural University, Sanya 572024, China.
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Zeng S, Zhang Z, Cheng X, Cai X, Cao M, Guo W. Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123402. [PMID: 37738767 DOI: 10.1016/j.saa.2023.123402] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp's reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.
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Affiliation(s)
- Shuochong Zeng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zongyi Zhang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaodong Cheng
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiao Cai
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Mengke Cao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wenchuan Guo
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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Pan L, Li H, Zhao J. Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123075. [PMID: 37423101 DOI: 10.1016/j.saa.2023.123075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023]
Abstract
Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.
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Affiliation(s)
- Liulei Pan
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Hao Li
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Juan Zhao
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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Zhang Z, Liu H, Wei Z, Lu M, Pu Y, Pan L, Zhang Z, Zhao J, Hu J. A transfer learning method for spectral model of moldy apples from different origins. Food Control 2023. [DOI: 10.1016/j.foodcont.2023.109731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Rapid Identification of Apple Maturity Based on Multispectral Sensor Combined with Spectral Shape Features. HORTICULTURAE 2022. [DOI: 10.3390/horticulturae8050361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The rapid and convenient detection of maturity is of great significance to determine the harvest time and postharvest storage conditions of apples. In this study, a portable visible and near-infrared (VIS/NIR) analysis device prototype was developed based on a multispectral sensor and applied to ‘Fuji’ apple maturity detection. The multispectral data of apples with maturity variation was measured, and the prediction model was established by a least-square support vector machine and linear discriminant analysis. Due to the low resolution of the multispectral data, regular preprocessing methods cannot improve the prediction accuracy. Instead, the spectral shape features (spectral ratio, spectral difference, and normalized spectral intensity difference) were used for preprocessing and model establishment, and the combination of the three features effectively improved the model performance with a prediction accuracy of 88.46%. In addition, the validation accuracy of the optimal model was 84.72%, and the area under curve (AUC) value of each maturity level was higher than 0.8972. The results show that the multispectral sensor is an appliable choice for the development of the portable detection device of apple maturity, and the data processing method proposed in this study provides a potential solution to improve the detection accuracy for multispectral sensors.
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