1
|
Xu W, Wei L, Cheng W, Yi X, Lin Y. Non-destructive assessment of soluble solids content in kiwifruit using hyperspectral imaging coupled with feature engineering. FRONTIERS IN PLANT SCIENCE 2024; 15:1292365. [PMID: 38357269 PMCID: PMC10864577 DOI: 10.3389/fpls.2024.1292365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
The maturity of kiwifruit is widely gauged by its soluble solids content (SSC), with accurate assessment being essential to guarantee the fruit's quality. Hyperspectral imaging offers a non-destructive alternative to traditional destructive methods for SSC evaluation, though its efficacy is often hindered by the redundancy and external disturbances of spectral images. This study aims to enhance the accuracy of SSC predictions by employing feature engineering to meticulously select optimal spectral features and mitigate disturbance effects. We conducted a comprehensive investigation of four spectral pre-processing and nine spectral feature selection methods, as components of feature engineering, to determine their influence on the performance of a linear regression model based on ordinary least squares (OLS). Additionally, the stacking generalization technique was employed to amalgamate the strengths of the two most effective models derived from feature engineering. Our findings demonstrate a considerable improvement in SSC prediction accuracy post feature engineering. The most effective model, when considering both feature engineering and stacking generalization, achieved an R M S E p of 0.721, a M A P E p of 0.046, and an R P D p of 1.394 in the prediction set. The study confirms that feature engineering, especially the careful selection of spectral features, and the stacking generalization technique are instrumental in bolstering SSC prediction in kiwifruit. This advancement enhances the application of hyperspectral imaging for quality assessment, offering benefits that extend across the agricultural industry.
Collapse
Affiliation(s)
- Wei Xu
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
| | - Liangzhuang Wei
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangwei Yi
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| | - Yandan Lin
- Institute for Electric Light Sources, School of Information Science and Technology, Fudan University, Shanghai, China
- Institute for Six-sector Economy, Fudan University, Shanghai, China
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| |
Collapse
|
2
|
Tian S, Liu W, Xu H. Improving the prediction performance of soluble solids content (SSC) in kiwifruit by means of near-infrared spectroscopy using slope/bias correction and calibration updating. Food Res Int 2023; 170:112988. [PMID: 37316062 DOI: 10.1016/j.foodres.2023.112988] [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: 01/31/2023] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 06/16/2023]
Abstract
Soluble solids content (SSC) is particularly important for kiwifruit, as it not only determines its flavor, but also helps assess its maturity. Visible/near-infrared (Vis/NIR) spectroscopy has been widely used to evaluate the SSC of kiwifruit. Still, the local calibration models may be ineffective for new batches of samples with biological variability, which limits the commercial application of this technology. Thus, a calibration model was developed using one batch of fruit and the prediction performance was tested with a different batch, which differs in origin and harvest time. Four calibration models were established with Batch 1 kiwifruit to predict SSC, which were based on full spectra (i.e., partial least squares regression (PLSR) model based on full spectra), continuous effective wavelengths (i.e., changeable size moving window-PLSR (CSMW-PLSR) model), and discrete effective wavelengths (i.e., competitive adaptive reweighted sampling-PLSR (CARS-PLSR) model and PLSR-variable importance in projection (PLSR-VIP) model) respectively. The Rv2 values of these four models in the internal validation set were 0.83, 0.92, 0.96, and 0.89, with corresponding RMSEV values of 1.08 %, 0.75 %, 0.56 %, and 0.89 %, and RPDv values of 2.49, 3.61, 4.80, and 3.02, respectively. Clearly, all four PLSR models performed acceptably in the validation set. However, these models performed very poorly in predicting the Batch 2 samples, with their RMSEP values all exceeding 1.5 %. Although the models could not be used to predict exact SSC, they could still interpret the SSC values of Batch 2 kiwifruit to some extent because the predicted SSC values could fit a specific line. To enable the CSMW-PLSR calibration model to predict the SSC of Batch 2 kiwifruit, the robustness of this model was improved by calibration updating and slope/bias correction (SBC). Different numbers of new samples were randomly selected for updating and SBC, and the minimum number of samples for updating and SBC was finally determined to be 30 and 20, respectively. After calibration updating and SBC, the new models had average Rp2, average RMSEP, and average RPDp values of 0.83 and 0.89, 0.69 % and 0.57 %, and 2.45 and 2.97, respectively, in the prediction set. Overall, the methods proposed in this study can effectively address the issue of poor performance of calibration models in predicting new samples with biological variability and make the models more robust, thus providing important guidance for the maintenance of SSC online detection models in practical applications.
Collapse
Affiliation(s)
- Shijie Tian
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China
| | - Wei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Huirong Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Key Laboratory of on Site Processing Equipment for Agricultural Products, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China.
| |
Collapse
|
3
|
Lytou AE, Tsakanikas P, Lymperi D, Nychas GJE. Rapid Assessment of Microbial Quality in Edible Seaweeds Using Sensor Techniques Based on Spectroscopy, Imaging Analysis and Sensors Mimicking Human Senses. SENSORS (BASEL, SWITZERLAND) 2022; 22:7018. [PMID: 36146366 PMCID: PMC9502184 DOI: 10.3390/s22187018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/09/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
The expansion of the seaweed aquaculture sector along with the rapid deterioration of these products escalates the importance of implementing rapid, real-time techniques for their quality assessment. Seaweed samples originating from Scotland and Ireland were stored under various temperature conditions for specific time intervals. Microbiological analysis was performed throughout storage to assess the total viable counts (TVC), while in parallel FT-IR spectroscopy, multispectral imaging (MSI) and electronic nose (e-nose) analyses were conducted. Machine learning models (partial least square regression (PLS-R)) were developed to assess any correlations between sensor and microbiological data. Microbial counts ranged from 1.8 to 9.5 log CFU/g, while the microbial growth rate was affected by origin, harvest year and storage temperature. The models developed using FT-IR data indicated a good prediction performance on the external test dataset. The model developed by combining data from both origins resulted in satisfactory prediction performance, exhibiting enhanced robustness from being origin unaware towards microbiological population prediction. The results of the model developed with the MSI data indicated a relatively good prediction performance on the external test dataset in spite of the high RMSE values, whereas while using e-nose data from both MI and SAMS, a poor prediction performance of the model was reported.
Collapse
|
4
|
Kim SY, Hong SJ, Kim E, Lee CH, Kim G. Application of ensemble neural-network method to integrated sugar content prediction model for citrus fruit using Vis/NIR spectroscopy. J FOOD ENG 2022. [DOI: 10.1016/j.jfoodeng.2022.111254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
|
5
|
Smart packaging temperature indicator based on encapsulated thermochromic material for the optimal watermelon taste. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2022. [DOI: 10.1007/s11694-022-01342-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
6
|
Nondestructive Methods for the Quality Assessment of Fruits and Vegetables Considering Their Physical and Biological Variability. FOOD ENGINEERING REVIEWS 2022. [DOI: 10.1007/s12393-021-09300-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
7
|
Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.04.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|
8
|
Luan X, Liu J, Liu F. Multilevel LASSO-based NIR temperature-correction modeling for viscosity measurement of bisphenol-A. ISA TRANSACTIONS 2020; 107:206-213. [PMID: 32741585 DOI: 10.1016/j.isatra.2020.07.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 07/14/2020] [Accepted: 07/14/2020] [Indexed: 06/11/2023]
Abstract
Temperature variation will affect the prediction accuracy when using the near-infrared (NIR) analytical model to measure the viscosity of bisphenol-A. In order to correct the effect of temperature on the prediction performance of NIR model, a multilevel least-absolute shrinkage and selection operator (LASSO) is proposed in this paper. The multilevel LASSO algorithm combines LASSO with the multilevel simultaneous component analysis (MLSCA) to enhance the robustness of the model under temperature changes and external disturbances. MLSCA is applied to decompose the molecular spectral data into two parts. One part denotes the property caused by temperature, the other means the changes of concentration. LASSO, a sparse regression model, is used to select the variables and perform the regularization to further enhance the robustness and interpretability of the model. Experimental results demonstrate the effectiveness of the proposed model in measuring bisphenol-A viscosity, which provides a more stable prediction result compared with the existing ones without temperature corrections.
Collapse
Affiliation(s)
- Xiaoli Luan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, PR China.
| | - Jin Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, PR China
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, 214122, PR China
| |
Collapse
|
9
|
Wang S, Tamura T, Kyouno N, Liu X, Zhang H, Akiyama Y, Chen JY. Rapid detection of quality of Japanese fermented soy sauce using near-infrared spectroscopy. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2020; 12:2347-2354. [PMID: 32930260 DOI: 10.1039/d0ay00521e] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This study investigated the feasibility of rapidly evaluating the final quality of Japanese fermented soy sauce (shoyu) using NIR spectroscopy and partial least-squares (PLS) regression. In total, 110 shoyu samples that had been entered in the annual soy sauce competition from 2016 to 2018 were collected and analyzed. The transmittance spectra (400-1800 nm) and the transflectance spectra (680-2500 nm) of these samples were acquired and processed by different pre-treatments. PLS regression was applied to the raw and processed spectra to construct models based on a calibration set (76 shoyu samples from 2016 and 2017) and to evaluate these models using a validation set (34 shoyu samples from 2018), according to their values for bias and root mean square error of prediction (RMSEP). The results showed that the models constructed using the full spectra of transflectance performed better than those using transmittance spectra. Comparing the influence of different regions in the transflectance spectra enabled the accuracy of the models to be improved. The model constructed from transflectance spectra from the 1800 to 2500 nm region using pre-treatment of second derivative was superior to the other models, with a bias value of -2 and the lowest RMSEP value of 13 in the validation set. To further narrow the wavelength range, the models constructed using the spectral region from 2050 to 2400 nm also showed a better performance for predicting the sensory quality of soy sauce products. This study has demonstrated that the NIR spectroscopy technique could be used as an alternative routine quality control procedure, which can rapidly and economically classify the quality of soy sauce products.
Collapse
Affiliation(s)
- Shuo Wang
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan.
| | - Takehiro Tamura
- Akita Prefectural Federation of Miso and Soy Sauce Manufacturers Cooperatives, Akita 010-0923, Japan
| | - Nobuyuki Kyouno
- Akita Prefectural Federation of Miso and Soy Sauce Manufacturers Cooperatives, Akita 010-0923, Japan
| | - Xiaofang Liu
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan.
| | - Han Zhang
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan.
| | - Yoshinobu Akiyama
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan.
| | - Jie Yu Chen
- Laboratory of Food Quality Science, Department of Biotechnology, Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan.
| |
Collapse
|
10
|
Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) Spectroscopy. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01530-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
|
11
|
Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures. Food Chem 2019; 278:314-321. [DOI: 10.1016/j.foodchem.2018.11.054] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 10/30/2018] [Accepted: 11/09/2018] [Indexed: 11/21/2022]
|
12
|
Liu J, Wei X, Zhang X, Qi Y, Zhang B, Liu H, Xiao P. A Comprehensive Comparative Study for the Authentication of the Kadsura Crude Drug. Front Pharmacol 2019; 9:1576. [PMID: 30740055 PMCID: PMC6357937 DOI: 10.3389/fphar.2018.01576] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 12/31/2018] [Indexed: 11/18/2022] Open
Abstract
The stems and roots of Kadsura species have been used as the folk medicine in Traditional Chinese medicine (TCM) and have good traditional efficacy and medicinal application with a long history. Among these species, K. coccinea, K. heteroclita and K. longipedunculata are the most widely distributed species in the regions of south and southwest China. Owing to their similar appearance, the crude drugs are often confusedly used by some folk doctors, even some pharmaceutical factories. To discriminate the crude drugs, haplotype analysis based on cpDNA markers and ITS was firstly employed in this study. Generic delimitation, interspecific interrelationships, and the identification of medicinal materials between K. longipedunculata and K. heteroclita remained unresolved by the existing molecular fragments. The original plant could be identified through the morphological character of flower, fruit and leaf. However, in most situation collectors have no chance to find out these characters due to lack of reproductive organs, and have no experience with the minor difference and transitional variation of leaf morphology. The chemical characterization show that the chemometric of chemical composition owned higher resolution to discriminate three herbs of Kadsura species. In conclusion, this integrative approach involving molecular phylogeny, morphology and chemical characterization could be applied for authentication of the Kadusra. Our study suggests the use of this comprehensive approach for accurate characterization of this closely related taxa as well as identifying the source plant and confused herbs of TCM.
Collapse
Affiliation(s)
- Jiushi Liu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Xueping Wei
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Engineering Research Center of Traditional Chinese Medicine Resources, Ministry of Education, Beijing, China
| | - Xiaoyi Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Yaodong Qi
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Engineering Research Center of Traditional Chinese Medicine Resources, Ministry of Education, Beijing, China
| | - Bengang Zhang
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Engineering Research Center of Traditional Chinese Medicine Resources, Ministry of Education, Beijing, China
| | - Haitao Liu
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine (Peking Union Medical College), Ministry of Education, Beijing, China
| | - Peigen Xiao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
- Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine (Peking Union Medical College), Ministry of Education, Beijing, China
| |
Collapse
|
13
|
Xu X, Xu H, Xie L, Ying Y. Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2018. [DOI: 10.1007/s11694-018-9964-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
14
|
Zhang B, Gu B, Tian G, Zhou J, Huang J, Xiong Y. Challenges and solutions of optical-based nondestructive quality inspection for robotic fruit and vegetable grading systems: A technical review. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.09.018] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
15
|
Luan X, Huang B, Sedghi S, Liu F. Probabilistic PCR based near-infrared modeling with temperature compensation. ISA TRANSACTIONS 2018; 81:46-51. [PMID: 29941290 DOI: 10.1016/j.isatra.2018.06.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 04/18/2018] [Accepted: 06/15/2018] [Indexed: 06/08/2023]
Abstract
Considering that temperature makes a difference to near-infrared spectrum, a probabilistic principle component regression (PPCR) based temperature compensation modeling strategy is investigated under the framework of maximum likelihood estimation. First, a PPCR model is established to extract the dynamic information of the spectra at designated experimental temperature. Then, by decomposing the temperature-induced spectral variation into the shift in horizontal direction and the drift in vertical direction, the quantitative expression between spectral variation and temperature change is derived. Based on the decomposition, the estimation of new latent variables that vary with temperature is derived according to the spectral data set collected at certain temperatures. Finally, for performance evaluation, applications of the theoretical results to bisphenol-A and gasoline-ethanol mixture illustrate the effectiveness and advantages of the developed techniques.
Collapse
Affiliation(s)
- Xiaoli Luan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation Jiangnan University, Wuxi, 214122, PR China; Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2G6, Canada.
| | - Biao Huang
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2G6, Canada
| | - Shabnam Sedghi
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2G6, Canada
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation Jiangnan University, Wuxi, 214122, PR China
| |
Collapse
|
16
|
Campos MI, Antolin G, Debán L, Pardo R. Assessing the influence of temperature on NIRS prediction models for the determination of sodium content in dry-cured ham slices. Food Chem 2018; 257:237-242. [PMID: 29622205 DOI: 10.1016/j.foodchem.2018.02.131] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 02/20/2018] [Accepted: 02/25/2018] [Indexed: 11/24/2022]
Abstract
Temperature fluctuations are a key factor in the development of prediction models using near infrared spectroscopy (NIRS). In the present study, this influence has been investigated and a methodology has been proposed to reduce the effect of sample temperature on NIRS model prediction of the sodium content in dry-cured ham slices. Spectra were taken directly from the slices using a remote measurement probe (for non-contact analysis) at three different temperature ranges: -12 °C to -5°C, -5°C to 10 °C and 10 °C to 20 °C. Local and global temperature compensation methods were established. Partial-least squares (PLS) regression was used as a chemometrics tool to perform the calibrations. The results showed that local models were sensitive to changes in temperature, while a global temperature model using sample spectra over the entire temperature range showed good prediction ability, reducing the error caused by temperature fluctuations to acceptable levels for practical applications.
Collapse
Affiliation(s)
- M Isabel Campos
- CARTIF Technology Center, Agrofood and Sustainable Processes Division, Parque Tecnológico de Boecillo, 205, 47151 Valladolid, Spain; Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain.
| | - Gregorio Antolin
- CARTIF Technology Center, Agrofood and Sustainable Processes Division, Parque Tecnológico de Boecillo, 205, 47151 Valladolid, Spain; Chemical Engineering and Environmental Technology Department, E.I.I. (School of Industrial Engineering), University of Valladolid, P° del Cauce 59, 47011 Valladolid, Spain
| | - Luis Debán
- Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain
| | - Rafael Pardo
- Analytical Chemistry Department, Faculty of Sciences, University of Valladolid, P° de Belén, 7, 47011 Valladolid, Spain
| |
Collapse
|
17
|
Zhang B, Dai D, Huang J, Zhou J, Gui Q, Dai F. Influence of physical and biological variability and solution methods in fruit and vegetable quality nondestructive inspection by using imaging and near-infrared spectroscopy techniques: A review. Crit Rev Food Sci Nutr 2017; 58:2099-2118. [DOI: 10.1080/10408398.2017.1300789] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Baohua Zhang
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| | - Dejian Dai
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| | - Jichao Huang
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| | - Jun Zhou
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| | - Qifa Gui
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| | - Fang Dai
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, PR China
| |
Collapse
|
18
|
Caramês ET, Alamar PD, Poppi RJ, Pallone JAL. Quality control of cashew apple and guava nectar by near infrared spectroscopy. J Food Compost Anal 2017. [DOI: 10.1016/j.jfca.2016.12.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
19
|
Zhou F, Peng J, Zhao Y, Huang W, Jiang Y, Li M, Wu X, Lu B. Varietal classification and antioxidant activity prediction of Osmanthus fragrans Lour. flowers using UPLC–PDA/QTOF–MS and multivariable analysis. Food Chem 2017; 217:490-497. [DOI: 10.1016/j.foodchem.2016.08.125] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Revised: 08/17/2016] [Accepted: 08/30/2016] [Indexed: 12/22/2022]
|
20
|
Xu W, Xie L, Ye Z, Gao W, Yao Y, Chen M, Qin J, Ying Y. Discrimination of Transgenic Rice containing the Cry1Ab Protein using Terahertz Spectroscopy and Chemometrics. Sci Rep 2015; 5:11115. [PMID: 26154950 PMCID: PMC4495602 DOI: 10.1038/srep11115] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 05/14/2015] [Indexed: 01/25/2023] Open
Abstract
Spectroscopic techniques combined with chemometrics methods have proven to be effective tools for the discrimination of objects with similar properties. In this work, terahertz time-domain spectroscopy (THz-TDS) combined with discriminate analysis (DA) and principal component analysis (PCA) with derivative pretreatments was performed to differentiate transgenic rice (Hua Hui 1, containing the Cry1Ab protein) from its parent (Ming Hui 63). Both rice samples and the Cry1Ab protein were ground and pressed into pellets for terahertz (THz) measurements. The resulting time-domain spectra were transformed into frequency-domain spectra, and then, the transmittances of the rice and Cry1Ab protein were calculated. By applying the first derivative of the THz spectra in conjunction with the DA model, the discrimination of transgenic from non-transgenic rice was possible with accuracies up to 89.4% and 85.0% for the calibration set and validation set, respectively. The results indicated that THz spectroscopic techniques and chemometrics methods could be new feasible ways to differentiate transgenic rice.
Collapse
Affiliation(s)
- Wendao Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Lijuan Xie
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Zunzhong Ye
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Weilu Gao
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Yang Yao
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Min Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Jianyuan Qin
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| | - Yibin Ying
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Rd., 310058 Hangzhou, PR China
| |
Collapse
|
21
|
ElMasry G, Nakauchi S. Noninvasive sensing of thermal treatments of Japanese seafood products using imaging spectroscopy. Int J Food Sci Technol 2015. [DOI: 10.1111/ijfs.12863] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Gamal ElMasry
- Department of Computer Science and Engineering; Toyohashi University of Technology; Toyohashi 441-8580 Japan
- Agricultural Engineering Department; Faculty of Agriculture; Suez Canal University; PO Box 41522 Ismailia Egypt
| | - Shigeki Nakauchi
- Department of Computer Science and Engineering; Toyohashi University of Technology; Toyohashi 441-8580 Japan
| |
Collapse
|
22
|
Wang A, Hu D, Xie L. Comparison of detection modes in terms of the necessity of visible region (VIS) and influence of the peel on soluble solids content (SSC) determination of navel orange using VIS–SWNIR spectroscopy. J FOOD ENG 2014. [DOI: 10.1016/j.jfoodeng.2013.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|