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Feng Y, Lv Y, Dong F, Chen Y, Li H, Rodas-González A, Wang S. Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for prediction of norfloxacin residues in mutton. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 322:124844. [PMID: 39053116 DOI: 10.1016/j.saa.2024.124844] [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: 12/13/2023] [Revised: 06/07/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
Norfloxacin is an antibacterial compound that belongs to the fluoroquinolone family. Currently, hyperspectral imaging (HSI) for the detection of antibiotic residues focuses mostly on individual systems. Attempts to integrate different HSI systems with complementary spectral ranges are still lacking. This study investigates the feasibility of applying data fusion strategies with two HSI techniques (Visible near-infrared and near-infrared) in combination to predict norfloxacin residue levels in mutton. Spectral data from the two spectral techniques were analyzed using partial least squares regression (PLSR), support vector regression (SVR) and stochastic configuration networks (SCN), respectively, and the two data fusion strategies were fused at the data level (low-level fusion) and feature level (middle-level fusion, mid-level fusion). The results indicated that the modeling performance of the two fused datasets was better than that of the individual systems. Mid-level fusion data achieved the best model based on uninformative variable elimination (UVE) combined with SCN, in which the determination coefficient of prediction set (R2p) of 0.9312, (root mean square error of prediction set) RMSEP of 0.3316 and residual prediction deviation (RPD) of 2.7434, in comparison with all others. Therefore, two HSI systems with complementary spectral ranges, combined with data fusion strategies and feature selection, could be used synergistically to improve the detection of norfloxacin residues. This study may provide a valuable reference for the non-destructive detection of antibiotic residues in meat.
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Affiliation(s)
- Yingjie Feng
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Fujia Dong
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Yue Chen
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Hui Li
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | | | - Songlei Wang
- College of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
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2
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Guo M, Lin H, Wang K, Cao L, Sui J. Data fusion of near-infrared and Raman spectroscopy: An innovative tool for non-destructive prediction of the TVB-N content of salmon samples. Food Res Int 2024; 189:114564. [PMID: 38876596 DOI: 10.1016/j.foodres.2024.114564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/21/2024] [Accepted: 05/26/2024] [Indexed: 06/16/2024]
Abstract
Total volatile basic nitrogen (TVB-N) serves as a crucial indicator for evaluating the freshness of salmon. This study aimed to achieve accurate and non-destructive prediction of TVB-N content in salmon fillets stored in multiple temperature settings (-20, 0, -4, 20 °C, and dynamic temperature) using near-infrared (NIR) and Raman spectroscopy. A partial least square support vector machine (LSSVM) regression model was established through the integration of NIR and Raman spectral data using low-level data fusion (LLDF) and mid-level data fusion (MLDF) strategies. Notably, compared to a single spectrum analysis, the LLDF approach provided the most accurate prediction model, achieving an R2P of 0.910 and an RMSEP of 1.922 mg/100 g. Furthermore, MLDF models based on 2D-COS and VIP achieved R2P values of 0.885 and 0.906, respectively. These findings demonstrated the effectiveness of the proposed method for precise quantitative detection of salmon TVB-N, laying a technical foundation for the exploration of similar approaches in the study of other meat products. This approach has the potential to assess and monitor the freshness of seafood, ensuring consumer safety and enhancing product quality.
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Affiliation(s)
- Minqiang Guo
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China; College of Food Science and Engineering, Xinjiang Institute of Technology, Aksu, Xinjiang 843100, China
| | - Hong Lin
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Kaiqiang Wang
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China.
| | - Limin Cao
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
| | - Jianxin Sui
- State Key Laboratory of Marine Food Processing & Safety Control, College of Food Science and Engineering, Ocean University of China, Qingdao, Shandong 266003, China
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3
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Dong F, Bi Y, Hao J, Liu S, Yi W, Yu W, Lv Y, Cui J, Li H, Xian J, Chen S, Wang S. A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM. Food Chem 2024; 440:138040. [PMID: 38103505 DOI: 10.1016/j.foodchem.2023.138040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 10/31/2023] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.
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Affiliation(s)
- Fujia Dong
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yongzhao Bi
- Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University (BTBU), Beijing 100048, China
| | - Jie Hao
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sijia Liu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Weiguo Yi
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Wenjie Yu
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Hui Li
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Jinhua Xian
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Sichun Chen
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China
| | - Songlei Wang
- School of Food Science and Engineering, Ningxia University, Yinchuan 750021, China.
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4
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Zhang Y, Zareef M, Rong Y, Lin H, Chen Q, Ouyang Q. Application of colorimetric sensor array coupled with chemometric methods for monitoring the freshness of snakehead fillets. Food Chem 2024; 439:138172. [PMID: 38091785 DOI: 10.1016/j.foodchem.2023.138172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/07/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
Total volatile basic nitrogen content (TVB-N) is an important index of freshness for snakehead. This paper attempted the feasibility of determining TVB-N content level in snakehead fillets by a colorimetric sensor array (CSA) composed of twelve porphyrin materials and eight pH indicators. The nine feature variables in RGB, HSV and CIE L*a*b* color spaces were obtained by differentiating the images of the CSA before and after exposure to the headspace-gas of the samples. Competitive adaptive reweighted sampling combined with partial least squares regression (CARS-PLS) was used to build the relationship between the TVB-N content and the feature variables of CSA, and to select meaningful color-sensitive materials. The results showed that CARS-PLS had a correlation coefficient of 0.9325 in the prediction set and selected 13 informative color-sensitive materials. This study demonstrated that the CSA with CARS-PLS algorithm could be used successfully to quantify and monitor the TVB-N in snakehead fillets.
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Affiliation(s)
- Yuxin Zhang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Hao Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Ocean Food and Biological Engineering, Jimei University, Xiamen 361021, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
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5
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Chen R, Li X, Li W, Yang R, Lu Y, You Z, Liu F. Crater-Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy. Foods 2024; 13:1083. [PMID: 38611387 PMCID: PMC11011736 DOI: 10.3390/foods13071083] [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: 03/02/2024] [Revised: 03/22/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024] Open
Abstract
Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater-spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater-spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater-spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
| | - Weijiao Li
- School of Chinese Material Medica, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
| | - Yi Lu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
| | - Zhengkai You
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (R.C.); (X.L.); (R.Y.); (Y.L.); (Z.Y.)
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6
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Yu Y, Chen W, Zhang H, Liu R, Li C. Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging. Foods 2024; 13:973. [PMID: 38611279 PMCID: PMC11011688 DOI: 10.3390/foods13070973] [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: 02/15/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024] Open
Abstract
The detection of the storage state of frozen meat, especially meat frozen-thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen-stored (F-S), frozen-thawed three times (F-T-3) and frozen-thawed five times (F-T-5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze-thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.
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Affiliation(s)
- Yuewen Yu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Wenliang Chen
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Hanwen Zhang
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Rong Liu
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Chenxi Li
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; (Y.Y.); (W.C.); (H.Z.)
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7
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Yang CB, Cai ZL, Li QZ, Tang F, Wu JJ, Yang J, Zhang YR, Li B, Yang P, Ye X, Yang LM. Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index. JOURNAL OF BIOPHOTONICS 2024; 17:e202300323. [PMID: 37769060 DOI: 10.1002/jbio.202300323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 09/30/2023]
Abstract
To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.
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Affiliation(s)
- Cheng-Bo Yang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | | | - Qing-Zhi Li
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Feng Tang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Jing-Jun Wu
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Jia Yang
- Sichuan Science City Hospital, Mianyang, China
| | | | - Bo Li
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Ping Yang
- Sichuan Science City Hospital, Mianyang, China
| | - Xin Ye
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
| | - Li-Ming Yang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang, China
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8
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Hao Y, Li X, Zhang C. Improving prediction model robustness with virtual sample construction for near-infrared spectra analysis. Anal Chim Acta 2023; 1279:341763. [PMID: 37827664 DOI: 10.1016/j.aca.2023.341763] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/22/2023] [Accepted: 08/28/2023] [Indexed: 10/14/2023]
Abstract
In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRRglob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRRmaj), but the CRR for minority class sample (CRRmin) has decreased. For the DCGAN method, the CRRglob, CRRmaj, and CRRmin were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model.
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Affiliation(s)
- Yong Hao
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China; Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang, 330013, China.
| | - Xiyan Li
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China
| | - Chengxiang Zhang
- School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China
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9
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Cao YM, Zhang Y, Yu ST, Wang KK, Chen YJ, Xu ZM, Ma ZY, Chen HL, Wang Q, Zhao R, Sun XQ, Li JT. Rapid and Non-Invasive Assessment of Texture Profile Analysis of Common Carp ( Cyprinus carpio L.) Using Hyperspectral Imaging and Machine Learning. Foods 2023; 12:3154. [PMID: 37685087 PMCID: PMC10486347 DOI: 10.3390/foods12173154] [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: 07/24/2023] [Revised: 08/11/2023] [Accepted: 08/14/2023] [Indexed: 09/10/2023] Open
Abstract
Hyperspectral imaging (HSI) has been applied to assess the texture profile analysis (TPA) of processed meat. However, whether the texture profiles of live fish muscle could be assessed using HSI has not been determined. In this study, we evaluated the texture profile of four muscle regions of live common carp by scanning the corresponding skin regions using HSI. We collected skin hyperspectral information from four regions of 387 scaled and live common carp. Eight texture indicators of the muscle corresponding to each skin region were measured. With the skin HSI of live common carp, six machine learning (ML) models were used to predict the muscle texture indicators. Backpropagation artificial neural network (BP-ANN), partial least-square regression (PLSR), and least-square support vector machine (LS-SVM) were identified as the optimal models for predicting the texture parameters of the dorsal (coefficients of determination for prediction (rp) ranged from 0.9191 to 0.9847, and the root-mean-square error for prediction ranged from 0.1070 to 0.3165), pectoral (rp ranged from 0.9033 to 0.9574, and RMSEP ranged from 0.2285 to 0.3930), abdominal (rp ranged from 0.9070 to 0.9776, and RMSEP ranged from 0.1649 to 0.3601), and gluteal (rp ranged from 0.8726 to 0.9768, and RMSEP ranged from 0.1804 to 0.3938) regions. The optimal ML models and skin HSI data were employed to generate visual prediction maps of TPA values in common carp muscles. These results demonstrated that skin HSI and the optimal models can be used to rapidly and accurately determine the texture qualities of different muscle regions in common carp.
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Affiliation(s)
- Yi-Ming Cao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Yan Zhang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Shuang-Ting Yu
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
- Chinese Academy of Agricultural Sciences, Beijing 100181, China
| | - Kai-Kuo Wang
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Ying-Jie Chen
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Zi-Ming Xu
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Zi-Yao Ma
- National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China; (K.-K.W.); (Y.-J.C.); (Z.-M.X.); (Z.-Y.M.)
| | - Hong-Lu Chen
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Qi Wang
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Ran Zhao
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Xiao-Qing Sun
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
| | - Jiong-Tang Li
- Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing 100041, China; (Y.-M.C.); (Y.Z.); (S.-T.Y.); (H.-L.C.); (Q.W.); (R.Z.); (X.-Q.S.)
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10
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Wang Z, An T, Wang W, Fan S, Chen L, Tian X. Qualitative and quantitative detection of aflatoxins B1 in maize kernels with fluorescence hyperspectral imaging based on the combination method of boosting and stacking. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 296:122679. [PMID: 37011441 DOI: 10.1016/j.saa.2023.122679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/17/2023] [Accepted: 03/26/2023] [Indexed: 06/19/2023]
Abstract
The most widespread, toxic, and harmful toxin is aflatoxins B1 (AFB1). The fluorescence hyperspectral imaging (HSI) system was employed for AFB1 detection in this study. This study developed the under sampling stacking (USS) algorithm for imbalanced data. The results indicated that the USS method combined with ANOVA for featured wavelength achieved the best performance with the accuracy of 0.98 for 20 or 50 μg /kg threshold using endosperm side spectra. As for the quantitative analysis, a specified function was used to compress AFB1 content, and the combination of boosting and stacking was used for regression. The support vector regression (SVR)-Boosting, Adaptive Boosting (AdaBoost), and extremely randomized trees (Extra-Trees)-Boosting were used as the base learner, while the K nearest neighbors (KNN) algorithm was used as the meta learner could obtain the best results, with the correlation coefficient of prediction (Rp) was 0.86. These results provided the basis for developing AFB1 detection and estimation technologies.
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Affiliation(s)
- Zheli Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Ting An
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Wenchao Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Shuxiang Fan
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Liping Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
| | - Xi Tian
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
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11
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Jin P, Fu Y, Niu R, Zhang Q, Zhang M, Li Z, Zhang X. Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy. Foods 2023; 12:2756. [PMID: 37509847 PMCID: PMC10379075 DOI: 10.3390/foods12142756] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/05/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky-Golay smoothing, SG; Savitzky-Golay 1 derivative, SG-1st; and Savitzky-Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R2p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R2p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.
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Affiliation(s)
- Peilin Jin
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yifan Fu
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
| | - Renzhong Niu
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Qi Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Mingyue Zhang
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Zhigang Li
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Xiaoshuan Zhang
- Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China
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12
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Rong Y, Zareef M, Liu L, Din ZU, Chen Q, Ouyang Q. Application of portable Vis-NIR spectroscopy for rapid detection of myoglobin in frozen pork. Meat Sci 2023; 201:109170. [PMID: 37004370 DOI: 10.1016/j.meatsci.2023.109170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 03/14/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023]
Abstract
Myoglobin content is considered as a crucial index to evaluate the quality of frozen pork. In this study, a portable visible and near-infrared (Vis-NIR) spectrometer combined with chemometrics was used to detect myoglobin content in frozen pork. Metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin were assessed spectrophotometrically. The raw Vis-NIR spectra of frozen pork samples were pre-processed using 1st derivatives (FD). Afterward, Synergy Interval Partial Least Square (Si-PLS) coupled Competitive Adaptive Reweighted Sampling algorithm (Si-CARS-PLS) was applied to select characteristic variables. The Si-CARS-PLS models revealed the probability of estimating myoglobin content in frozen pork, with predictive correlation coefficients (Rp) for metmyoglobin, deoxymyoglobin, oxymyoglobin, and total myoglobin as 0.9095, 0.9004, 0.8578, and 0.9133, respectively. The findings of this study showed that Vis-NIR spectroscopy coupled with Si-CARS-PLS is a promising method and offered a way forward for determining the myoglobin content in frozen pork.
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Affiliation(s)
- Yanna Rong
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Lihua Liu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Zia Ud Din
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China
| | - Qin Ouyang
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, PR China.
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13
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Zuo J, Peng Y, Li Y, Zou W, Chen Y, Huo D, Chao K. Nondestructive detection of nutritional parameters of pork based on NIR hyperspectral imaging technique. Meat Sci 2023; 202:109204. [PMID: 37146500 DOI: 10.1016/j.meatsci.2023.109204] [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: 11/29/2022] [Revised: 03/22/2023] [Accepted: 04/24/2023] [Indexed: 05/07/2023]
Abstract
Nondestructive detection of the nutritional parameters of pork is of great importance. This study aimed to investigate the feasibility of applying hyperspectral image technology to detect the nutrient content and distribution of pork nondestructively. Hyperspectral cubes of 100 pork samples were collected using a line-scan hyperspectral system, the effects of different preprocessing methods on the modeling effects were compared and analyzed, the feature wavelengths of fat and protein were extracted, and the full-wavelength model was optimized using the regressor chains (RC) algorithm. Finally, pork's fat, protein, and energy value distributions were visualized using the best prediction model. The results showed that standard normal variate was more effective than other preprocessing methods, the feature wavelengths extracted by the competitive adaptive reweighted sampling algorithm had better prediction performance, and the protein model prediction performance was optimized after using the RC algorithm. The best prediction models were developed, with the correlation coefficient of prediction (RP) = 0.929, the root mean square error in prediction (RMSEP) = 0.699% and residual prediction deviation (RPD) = 2.669 for fat, and RP = 0.934, RMSEP = 0.603% and RPD = 2.586 for protein. The pseudo-color maps were helpful for the analysis of nutrient distribution in pork. Hyperspectral image technology can be a fast, nondestructive, and accurate tool for quantifying the composition and assessing the distribution of nutrients in pork.
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Affiliation(s)
- Jiewen Zuo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yankun Peng
- College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Yongyu Li
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Wenlong Zou
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Yahui Chen
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Daoyu Huo
- College of Engineering, China Agricultural University, Beijing 100083, China
| | - Kuanglin Chao
- Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD 20705, United States
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14
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Liu S, Dong F, Hao J, Qiao L, Guo J, Wang S, Luo R, Lv Y, Cui J. Combination of hyperspectral imaging and entropy weight method for the comprehensive assessment of antioxidant enzyme activity in Tan mutton. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122342. [PMID: 36682252 DOI: 10.1016/j.saa.2023.122342] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/17/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
The antioxidant enzymes play the crucial role in inhibiting mutton spoilage. In this study, visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) combined with entropy weight method (EWM) was developed for the first time to evaluate the antioxidant properties of Tan mutton. The comprehensive index of antioxidant enzymes (AECI) consisting of peroxidase (49.34%), catalase (37.97%) and superoxidase (12.69%) was constructed by the EWM. Partial least squares regression, least squares support vector machine and artificial neural networks (ANN) were developed based on characteristic wavelengths extracted by successful projections algorithm, uninformative variable selection, iteratively retains informative variables (IRIV), regression coefficient and competitive adaptive reweighted sampling (CARS). The textural features (TF) were extracted by the gray level co-occurrence matrix and fused with the spectral data to establish models. Visualization of the changes in antioxidant enzyme activity was constructed from the optimal model. In addition, two-dimensional correlation spectra (2D-COS) with AECI as a perturbation variable was used to identify spectral features, revealing chemical bond changes order under the characteristic peaks at 612-799-473-708-559 nm. The results showed that the IRIV-CARS-TF-ANN model performed the best, with prediction set coefficient of determination (RP2) of 0.8813, which improved 2.12%, 1.11% and 2.77% over the RP2 of full band, IRIV and IRIV-CARS, respectively. It was suggested that fusion data of HSI may effectively predict the activity of antioxidant enzymes in Tan mutton.
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Affiliation(s)
- Sijia Liu
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Fujia Dong
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jie Hao
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Lu Qiao
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jianhong Guo
- School of Chemical & Biological Engineering, Yinchuan University of Energy, Yinchuan 750021, China
| | - Songlei Wang
- School of Food & Wine, Ningxia University, Yinchuan 750021, China.
| | - Ruiming Luo
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food & Wine, Ningxia University, Yinchuan 750021, China
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15
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Li Y, Bai X, Zhao M, Wang H, Feng J, Xia X, Liu Q. Sodium alginate edible coating to reduce oil absorption of French fries with maintaining overall acceptability: Based on a water replacement mechanism. Int J Biol Macromol 2023; 236:124042. [PMID: 36924874 DOI: 10.1016/j.ijbiomac.2023.124042] [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: 12/26/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023]
Abstract
The effect of sodium alginate (SA) coating on the oil content and quality of fries was evaluated, and the inhibitory mechanism of SA on oil absorption was analyzed based on the water replacement theory. Compared to uncoated samples, the penetrated surface oil (PSO), structure oil (STO), and total oil (TO) contents, a*, and b* of coated fries decreased, whereas moisture content, L* and hardness increased with no significant difference revealed by sensory evaluation of all samples. The water contact angle of the films correlated negatively with the water content and hardness of the fries. In contrast, it correlated positively with PSO, STO, and TO contents. The TO content of fries with 1 % SA film which had a compact microstructure, was the lowest, reduced by 52.5 % compared to the control sample. SA coating reduces the pores and roughness on the fries' surface, which inhibits the oil from penetrating into the samples. SA coating decreased the T21, T22, and pores of the starch, and increased the P2b, P21, relative crystallinity, and ΔH significantly (P < 0.05). Therefore, SA coating inhibits the oil absorption in fries by reducing water evaporation which is attributed to the increase in double helices and crystallinity of starch.
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Affiliation(s)
- Ying Li
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Xue Bai
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Mengna Zhao
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Hui Wang
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Jia Feng
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China
| | - Xiufang Xia
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
| | - Qian Liu
- College of Food Science, Northeast Agricultural University, Harbin, Heilongjiang 150030, China.
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16
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Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts. Foods 2023; 12:foods12051069. [PMID: 36900582 PMCID: PMC10000443 DOI: 10.3390/foods12051069] [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: 02/10/2023] [Revised: 02/22/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390-1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R2 value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications.
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17
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Saha D, Senthilkumar T, Sharma S, Singh CB, Manickavasagan A. Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2022.104938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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18
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Rapid determination of protein, starch and moisture contents in wheat flour by near-infrared hyperspectral imaging. J Food Compost Anal 2023. [DOI: 10.1016/j.jfca.2023.105134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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19
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Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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20
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Dong F, Bi Y, Hao J, Liu S, Lv Y, Cui J, Wang S, Han Y, Rodas-González A. A Combination of Near-Infrared Hyperspectral Imaging with Two-Dimensional Correlation Analysis for Monitoring the Content of Alanine in Beef. BIOSENSORS 2022; 12:bios12111043. [PMID: 36421161 PMCID: PMC9688476 DOI: 10.3390/bios12111043] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 05/31/2023]
Abstract
Alanine (Ala), as the most important free amino acid, plays a significant role in food taste characteristics and human health regulation. The feasibility of using near-infrared hyperspectral imaging (NIR-HSI) combined with two-dimensional correlation spectroscopy (2D-COS) analysis to predict beef Ala content quickly and nondestructively is first proposed in this study. With Ala content as the external disturbance condition, the sequence of chemical bond changes caused by synchronous and asynchronous correlation spectrum changes in 2D-COS was analyzed, and local sensitive variables closely related to Ala content were obtained. On this basis, the simplified linear, nonlinear, and artificial neural network models developed by the weighted coefficient based on the feature wavelength extraction method were compared. The results show that with the change in Ala content in beef, the double-frequency absorption of the C-H bond of CH2 in the chemical bond sequence occurred prior to the third vibration of the C=O bond and the first stretching of O-H in COOH. Furthermore, the wavelength within the 1136-1478 nm spectrum range was obtained as the local study area of Ala content. The linear partial least squares regression (PLSR) model based on effective wavelengths was selected by competitive adaptive reweighted sampling (CARS) from 2D-COS analysis, and provided excellent results (R2C of 0.8141, R2P of 0.8458, and RPDp of 2.54). Finally, the visual distribution of Ala content in beef was produced by the optimal simplified combination model. The results show that 2D-COS combined with NIR-HSI could be used as an effective method to monitor Ala content in beef.
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Affiliation(s)
- Fujia Dong
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yongzhao Bi
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Jie Hao
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Sijia Liu
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yu Lv
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Jiarui Cui
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Songlei Wang
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Yafang Han
- School of Food and Wine, Ningxia University, Yinchuan 750021, China
| | - Argenis Rodas-González
- Department of Animal Science, Faculty of Agricultural and Food Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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21
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Wu X, Liang X, Wang Y, Wu B, Sun J. Non-Destructive Techniques for the Analysis and Evaluation of Meat Quality and Safety: A Review. Foods 2022; 11:3713. [PMID: 36429304 PMCID: PMC9689883 DOI: 10.3390/foods11223713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/22/2022] Open
Abstract
With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
| | - Xinyue Liang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Yixuan Wang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Jun Sun
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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22
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Pu H, Wei Q, Sun DW. Recent advances in muscle food safety evaluation: Hyperspectral imaging analyses and applications. Crit Rev Food Sci Nutr 2022; 63:1297-1313. [PMID: 36123794 DOI: 10.1080/10408398.2022.2121805] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As there is growing interest in process control for quality and safety in the meat industry, by integrating spectroscopy and imaging technologies into one system, hyperspectral imaging, or chemical or spectroscopic imaging has become an alternative analytical technique that can provide the spatial distribution of spectrum for fast and nondestructive detection of meat safety. This review addresses the configuration of the hyperspectral imaging system and safety indicators of muscle foods involving biological, chemical, and physical attributes and other associated hazards or poisons, which could cause safety problems. The emphasis focuses on applications of hyperspectral imaging techniques in the safety evaluation of muscle foods, including pork, beef, lamb, chicken, fish and other meat products. Although HSI can provide the spatial distribution of spectrum, characterized by overtones and combinations of the C-H, N-H, and O-H groups using different combinations of a light source, imaging spectrograph and camera, there still needs improvement to overcome the disadvantages of HSI technology for further applications at the industrial level.
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Affiliation(s)
- Hongbin Pu
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Qingyi Wei
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China
| | - Da-Wen Sun
- School of Food Science and Engineering, South China University of Technology, Guangzhou, China.,Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou, China.,Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, & Guangdong Province Engineering Laboratory for Intelligent Cold Chain Logistics Equipment for Agricultural Products, Guangzhou Higher Education Mega Centre, Guangzhou, China.,Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Ireland
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23
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Wan G, Fan S, Liu G, He J, Wang W, Li Y, lijuan Cheng, Ma C, Guo M. Fusion of spectra and texture data of hyperspectral imaging for prediction of myoglobin content in nitrite-cured mutton. Food Control 2022. [DOI: 10.1016/j.foodcont.2022.109332] [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]
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24
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Alshehri S, Alqarni M, Namazi NI, Naguib IA, Venkatesan K, Mosaad YO, Pishnamazi M, Alsubaiyel AM, Abourehab MAS. Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug. Sci Rep 2022; 12:13106. [PMID: 35907929 PMCID: PMC9338975 DOI: 10.1038/s41598-022-17350-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/25/2022] [Indexed: 11/09/2022] Open
Abstract
These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.
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Affiliation(s)
- Sameer Alshehri
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Mohammed Alqarni
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nader Ibrahim Namazi
- Pharmaceutics and Pharmaceutical Technology Department, College of Pharmacy, Taibah University, Al Madinah Al Munawarah, 30001, Saudi Arabia
| | - Ibrahim A Naguib
- Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Kumar Venkatesan
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, 62529, Kingdom of Saudi Arabia
| | - Yasser O Mosaad
- Department of Pharmacy Practice and Clinical Pharmacy, Faculty Pharmacy, Future Unibversity in Egypt, New Cairo, 11835, Egypt
| | - Mahboubeh Pishnamazi
- Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam. .,The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam.
| | - Amal M Alsubaiyel
- Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah, 52571, Saudi Arabia
| | - Mohammed A S Abourehab
- Department of Pharmaceutics, Faculty of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia.,Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Minia University, Minia, 61519, Egypt
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Establishment of a rapid detection model for the sensory quality and components of Yuezhou Longjing tea using near-infrared spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhang J, Guo M, Liu G. Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR). J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Development of multiple machine-learning computational techniques for optimization of heterogenous catalytic biodiesel production from waste vegetable oil. ARAB J CHEM 2022. [DOI: 10.1016/j.arabjc.2022.103843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
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