1
|
Yang X, Zhu L, Huang X, Zhang Q, Li S, Chen Q, Wang Z, Li J. Determination of the Soluble Solids Content in Korla Fragrant Pears Based on Visible and Near-Infrared Spectroscopy Combined With Model Analysis and Variable Selection. FRONTIERS IN PLANT SCIENCE 2022; 13:938162. [PMID: 35874018 PMCID: PMC9298609 DOI: 10.3389/fpls.2022.938162] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the application of portable devices is more common. The construction of an accurate and stable prediction model is the key for the successful application of the device. In this study, the visible and near-infrared (Vis/NIR) spectra of Korla fragrant pears were collected by a commercial portable measurement device. Different pretreatment methods were used to preprocess the raw spectra, and the partial least squares (PLS) model was constructed to predict the SSC of pears for the determination of the appropriate pretreatment method. Subsequently, PLS and least squares support vector machine (LS-SVM) models were constructed based on the preprocessed full spectra. A new combination (BOSS-SPA) of bootstrapping soft shrinkage (BOSS) and successive projections algorithm (SPA) was used for variable selection. For comparison, single BOSS and SPA were also used for variable selection. Finally, three types of models, namely, PLS, LS-SVM, and multiple linear regression (MLR), were constructed based on different input variables. Comparing the prediction performance of all models, it showed that the BOSS-SPA-PLS model based on 17 variables obtained the best SSC assessment ability with r p of 0.94 and RMSEP of 0.27 °Brix. The overall result indicated that portable measurement with Vis/NIR spectroscopy can be used for the detection of SSC in Korla fragrant pears.
Collapse
Affiliation(s)
- Xuhai Yang
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
- Xinjiang Production & Construction Crops, Key Laboratory of Korla Fragrant Pear Germplasm Innovation and Quality Improvement and Efficiency Increment, Shihezi, China
| | - Lichun Zhu
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Xiao Huang
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Qian Zhang
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Sheng Li
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Qiling Chen
- Xinjiang Production & Construction Crops, Key Laboratory of Korla Fragrant Pear Germplasm Innovation and Quality Improvement and Efficiency Increment, Shihezi, China
| | - Zhendong Wang
- Xinjiang Production & Construction Crops, Key Laboratory of Korla Fragrant Pear Germplasm Innovation and Quality Improvement and Efficiency Increment, Shihezi, China
| | - Jingbin Li
- Xinjiang Production and Construction Corps, Key Laboratory of Modern Agricultural Machinery, College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| |
Collapse
|
2
|
Ju J, Zheng H, Xu X, Guo Z, Zheng Z, Lin M. Classification of jujube defects in small data sets based on transfer learning. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05715-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.
Collapse
|
3
|
Potential of Near-Infrared (NIR) Spectroscopy and Hyperspectral Imaging for Quality and Safety Assessment of Fruits: an Overview. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01609-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
|
4
|
Yin W, Zhang C, Zhu H, Zhao Y, He Y. Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries. PLoS One 2017; 12:e0180534. [PMID: 28704423 PMCID: PMC5509235 DOI: 10.1371/journal.pone.0180534] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/02/2017] [Indexed: 01/26/2023] Open
Abstract
Near-infrared (874-1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972-1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.
Collapse
Affiliation(s)
- Wenxin Yin
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Hongyan Zhu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yanru Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
- * E-mail:
| |
Collapse
|
5
|
Application of Long-Wave Near Infrared Hyperspectral Imaging for Measurement of Soluble Solid Content (SSC) in Pear. FOOD ANAL METHOD 2016. [DOI: 10.1007/s12161-016-0498-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
6
|
Fruit quality evaluation using spectroscopy technology: a review. SENSORS 2015; 15:11889-927. [PMID: 26007736 PMCID: PMC4481958 DOI: 10.3390/s150511889] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Revised: 05/14/2015] [Accepted: 05/18/2015] [Indexed: 11/26/2022]
Abstract
An overview is presented with regard to applications of visible and near infrared (Vis/NIR) spectroscopy, multispectral imaging and hyperspectral imaging techniques for quality attributes measurement and variety discrimination of various fruit species, i.e., apple, orange, kiwifruit, peach, grape, strawberry, grape, jujube, banana, mango and others. Some commonly utilized chemometrics including pretreatment methods, variable selection methods, discriminant methods and calibration methods are briefly introduced. The comprehensive review of applications, which concentrates primarily on Vis/NIR spectroscopy, are arranged according to fruit species. Most of the applications are focused on variety discrimination or the measurement of soluble solids content (SSC), acidity and firmness, but also some measurements involving dry matter, vitamin C, polyphenols and pigments have been reported. The feasibility of different spectral modes, i.e., reflectance, interactance and transmittance, are discussed. Optimal variable selection methods and calibration methods for measuring different attributes of different fruit species are addressed. Special attention is paid to sample preparation and the influence of the environment. Areas where further investigation is needed and problems concerning model robustness and model transfer are identified.
Collapse
|
7
|
Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality. FOOD ANAL METHOD 2015. [DOI: 10.1007/s12161-015-0153-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
8
|
Liu G, He J, Wang S, Luo Y, Wang W, Wu L, Si Z, He X. Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit. INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2015. [DOI: 10.1080/10942912.2014.923439] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
9
|
Pan L, Zhu Q, Lu R, McGrath JM. Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy. Food Chem 2015; 167:264-71. [DOI: 10.1016/j.foodchem.2014.06.117] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 06/10/2014] [Accepted: 06/29/2014] [Indexed: 11/28/2022]
|
10
|
Maniwara P, Nakano K, Boonyakiat D, Ohashi S, Hiroi M, Tohyama T. The use of visible and near infrared spectroscopy for evaluating passion fruit postharvest quality. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2014.06.028] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
11
|
Kramchote S, Nakano K, Kanlayanarat S, Ohashi S, Takizawa K, Bai G. Rapid determination of cabbage quality using visible and near-infrared spectroscopy. Lebensm Wiss Technol 2014. [DOI: 10.1016/j.lwt.2014.07.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
12
|
Hui G, Jin J, Deng S, Ye X, Zhao M, Wang M, Ye D. Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose. Food Chem 2014; 170:484-91. [PMID: 25306374 DOI: 10.1016/j.foodchem.2014.08.009] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 08/02/2014] [Accepted: 08/05/2014] [Indexed: 10/24/2022]
Abstract
Winter jujube (Zizyphus jujuba Mill.) quality forecasting method utilising electronic nose (EN) and double-layered cascaded series stochastic resonance (DCSSR) was investigated. EN responses to jujubes stored at room temperature were continuously measured for 8 days. Jujubes' physical/chemical indexes, such as firmness, colour, total soluble solids (TSS), and ascorbic acid (AA), were synchronously examined. Examination results indicated that jujubes were getting ripe during storage. EN measurement data was processed by stochastic resonance (SR) and DCSSR. SR and DCSSR output signal-to-noise ratio (SNR) maximums (SNR-MAX) discriminated jujubes under different storage time successfully. Multiple variable regression (MVR) results between physical/chemical indexes and SR/DCSSR eigen values demonstrated that DCSSR eigen values were more suitable for jujube quality determination. Quality forecasting model was developed using non-linear fitting regression of DCSSR eigen values. Validating experiments demonstrated that forecasting accuracy of this model is 97.35%. This method also presented other advantages including fast response, non-destructive, etc.
Collapse
Affiliation(s)
- Guohua Hui
- School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China; College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China.
| | - Jiaojiao Jin
- College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Shanggui Deng
- College of Food and Pharmacy, Zhejiang Ocean University, 316000 Zhoushan, China
| | - Xiao Ye
- College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Mengtian Zhao
- College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Minmin Wang
- College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Dandan Ye
- College of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou 310018, China
| |
Collapse
|
13
|
PUANGSOMBUT ARTHIT, PATHAVEERAT SIWALAK, TERDWONGWORAKUL ANUPUN, PUANGSOMBUT KAEWKARN. EVALUATION OF INTERNAL QUALITY OF FRESH-CUT POMELO USING VIS/NIR TRANSMITTANCE. J Texture Stud 2012. [DOI: 10.1111/j.1745-4603.2012.00354.x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|