Journal of Food Composition and Analysis最新文献_第3页

Robust application of a chemometric model based on the relationships between 10 volatile compounds and sensory attributes to support the panel test in virgin olive oil quality classification in olive oil companies

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-12 DOI: 10.1016/j.jfca.2025.107362

Lorenzo Cecchi , Marzia Migliorini , Irene Digiglio , Tommaso Ugolini , Serena Trapani , Bruno Zanoni , Nadia Mulinacci , Fabrizio Melani

{"title":"Robust application of a chemometric model based on the relationships between 10 volatile compounds and sensory attributes to support the panel test in virgin olive oil quality classification in olive oil companies","authors":"Lorenzo Cecchi , Marzia Migliorini , Irene Digiglio , Tommaso Ugolini , Serena Trapani , Bruno Zanoni , Nadia Mulinacci , Fabrizio Melani","doi":"10.1016/j.jfca.2025.107362","DOIUrl":"10.1016/j.jfca.2025.107362","url":null,"abstract":"<div><div>Several approaches have been proposed to support the panel test in virgin olive oil classification, but none of them is currently applied in olive oil companies. Aim of this study was the robust application of a chemometric model in a big olive oil company. The application on 244 samples of the PCA-LDA model developed in 2019, based on volatile profile by HS-SPME-GC-MS, gave unsatisfactory results, pointing out critical issues relating to the training-set, variable selection and validation. Therefore, a new <em>t-test-FwS-LDA</em> model was developed; it was based on a very wide dataset (approx. 1800 samples from 6 different production years) and on an algorithm for a stepwise selection of variables. The crucial role of the production year has been proven and included in the model. Ten volatile molecules were thus selected coming from both the lipoxygenase pathway and several virgin olive oil sensory defects. The new model was two-fold validated with 53 and 273 samples coming from production years belonging and not belonging to the training-set, respectively, with very satisfactory results (>90 % and 80 % correct classification, respectively). Finally, the study indicated that for routinary application of the model, year-by-year updating of training-set and variable selection is required.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107362"},"PeriodicalIF":4.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of bissap calyces and bissap juices adulteration with sorghum leaves using NIR spectroscopy and VIS/NIR spectroscopy

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-12 DOI: 10.1016/j.jfca.2025.107358

John-Lewis Zinia Zaukuu , Nelson Quarshie Attipoe , Patricia Bourba Korneh , Eric Tetteh Mensah , Donald Bimpong , Lois Adofowaa Amponsah

{"title":"Detection of bissap calyces and bissap juices adulteration with sorghum leaves using NIR spectroscopy and VIS/NIR spectroscopy","authors":"John-Lewis Zinia Zaukuu , Nelson Quarshie Attipoe , Patricia Bourba Korneh , Eric Tetteh Mensah , Donald Bimpong , Lois Adofowaa Amponsah","doi":"10.1016/j.jfca.2025.107358","DOIUrl":"10.1016/j.jfca.2025.107358","url":null,"abstract":"<div><div>Adulteration of bissap calyces and juices (<em>‘sobolo’</em>) with sorghum leaves presents a potential decrease in nutritional benefits to consumers. This study investigated the detection of bissap calyces and juices adulteration with sorghum leaves using near-infrared (NIR) and ultraviolet-visible (VIS/NIR) spectroscopy. Eight samples of bissap calyces-sorghum leaf formulations were prepared using eight concentrations (0, 5, 10, 20, 30, 40, 50 and 100 %) w/w. Each sample was prepared in cut, whole and powder forms, respectively. The samples were then used to prepare bissap juices in triplicates. The bissap calyces-sorghum leaf formulations were analyzed with NIRS, while bissap juices were analyzed with VIS/NIR spectroscopy. Results from the physicochemical analysis showed that the unadulterated samples had lower pH and higher (brix, titratable acidity, and total phenolic content), with no intense color change for all forms when compared to adulterated samples. PCA showed no difference between adulterated and unadulterated samples based on forms and concentrations. LDA showed a 100 % classification for all cut samples and some misclassifications for whole and powder samples for both NIR and VIS/NIR spectroscopy. Also, there were observable differences between adulterated and unadulterated juices produced from cut forms. PLSR models predicted different concentrations of adulterants present in both bissap calyces and juices. Bissap juices adulterated with sorghum leaves are not easily detectable and have reduced concentrations of some beneficial nutritional compounds present in sobolo. NIR and VIS/NIR spectroscopy, combined with chemometric techniques such as PCA, LDA, and PLSR, as a rapid detection technique, showed good potential for sobolo authentication.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107358"},"PeriodicalIF":4.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthesis and characterization of NiO nanoflower for dispersive micro solid phase extraction of zinc from water and food samples

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107348

Dilek Topaloğlu , Furkan Uzcan , Nebiye Kizil , Buse Bozan Beydagi , Duygu Erkmen Erbilgin , Erkan Basaran , Mehmet Lütfi Yola , Mustafa Soylak

{"title":"Synthesis and characterization of NiO nanoflower for dispersive micro solid phase extraction of zinc from water and food samples","authors":"Dilek Topaloğlu , Furkan Uzcan , Nebiye Kizil , Buse Bozan Beydagi , Duygu Erkmen Erbilgin , Erkan Basaran , Mehmet Lütfi Yola , Mustafa Soylak","doi":"10.1016/j.jfca.2025.107348","DOIUrl":"10.1016/j.jfca.2025.107348","url":null,"abstract":"<div><div>This study introduces a dispersive micro solid phase extraction (DµSPE) method utilizing NiO nanoflowers for the analysis of zinc in food, environmental, and wastewater samples, employing Inductively Coupled Plasma- Optical Emission Spectrometry (ICP-OES). The synthesized NiO nanoflower-like nanoparticle was characterized through Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM). Additionally, various parameters including pH, sample volume, adsorbent quantity, and extraction time were investigated to optimize the NiO nanoflower-based SPME (NiO nanoflower-SPME) method. The analytical performance metrics, specifically the limit of detection (LOD), limit of quantification (LOQ), and relative standard deviation (RSD), were determined to be 0.77 µg L<sup>−1</sup>, 2.56 µg L<sup>−1</sup>, and 3.9 %, respectively. Furthermore, addition-recovery studies conducted on real samples, along with analyses of standard reference materials, were performed to validate the accuracy of the method. With these results, it was concluded that the NiO nanoflower-SPME method is crucial for the analysis of zinc in real samples due to the fact that the complex matrix environment complicates the analysis.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107348"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State-of-the-art review of morel: From chemistry to nutrition and health benefits 羊肚菌的最新研究成果：从化学到营养和健康益处

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107351

Chengxuan Xu , Lijuan Qian , Qi Meng , Yujun Sun

{"title":"State-of-the-art review of morel: From chemistry to nutrition and health benefits","authors":"Chengxuan Xu , Lijuan Qian , Qi Meng , Yujun Sun","doi":"10.1016/j.jfca.2025.107351","DOIUrl":"10.1016/j.jfca.2025.107351","url":null,"abstract":"<div><div><em>Morchella esculenta</em> (L.) Pers. (M. <em>esculenta</em>), commonly known as the morel, is a highly esteemed edible and medicinal mushroom renowned for its distinctive flavor and diverse health benefits. This species is rich in essential nutrients and bioactive compounds, including proteins, carbohydrates, vitamins, and minerals, which contribute to its high nutritional value and unique flavor profile. Notably, morels contain an array of bioactive constituents such as polysaccharides, polyphenols, alkaloids, saponins, terpenoids, quinones, lignocellulosic enzymes, and lipoxygenase. These compounds underpin the diverse bioactivities attributed to morels, including immunomodulation, antioxidation, organ protection, lipid and glucose homeostasis regulation, anti-cancer effects, and mitigation of chemotherapy-induced toxicity. This review comprehensively summarizes the key nutrients and bioactive compounds present in morels, detailing their extraction methods and subsequent analyses. The insights provided aim to support potential industrial applications of morels, particularly in the development of functional foods. Furthermore, this review explores the various bioactivities of morels and their underlying molecular mechanisms, contributing to a deeper understanding of this valuable fungal resource.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107351"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A cumulative learning method for pixel-level hyperspectral detection of aflatoxins on peanuts using convolutional neural network

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107356

Yifan Zhao , Hongfei Zhu , Limiao Deng , Zhongzhi Han

引用次数: 0

Beef quality dual-label classification incorporating texture and multihead map attention mechanisms 结合纹理和多头图关注机制的牛肉质量双标签分类法

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107360

Runzhe Zhang, Weiming Shi, Yueyang Pan, Yifan Zhao, Zhenlu Hua, Yanshen Zhao, Qiang Pan, Zhongzhi Han

{"title":"Beef quality dual-label classification incorporating texture and multihead map attention mechanisms","authors":"Runzhe Zhang, Weiming Shi, Yueyang Pan, Yifan Zhao, Zhenlu Hua, Yanshen Zhao, Qiang Pan, Zhongzhi Han","doi":"10.1016/j.jfca.2025.107360","DOIUrl":"10.1016/j.jfca.2025.107360","url":null,"abstract":"<div><div>With the increasing consumer demand for high-quality and safe beef, there is an urgent need for advanced scientific testing methods to ensure product quality. This study introduces a rapid and accurate dual-label classification system for beef cuts and marbling grades, combining a multi-head attention mechanism and contrast features extracted from the Gray-Level Co-occurrence Matrix (GLCM), significantly improving classification accuracy. A comparison of different ResNet models revealed that ResNet50 achieved the highest classification accuracies, 85.65 % and 85.03 %, respectively. Building on the ResNet50 model, GLCM was used to extract contrast features in four directions from beef images, followed by feature fusion. Compared to SE and non-corresponding multi-head Graph Attention Networks (GAT), a multi-head GAT focusing on GLCM texture features in each direction was selected, with these features fused one-to-one with the fully connected layer features of RGB images and incorporating a feature fusion mechanism. Our model achieved 93.5 % accuracy in cut classification and 92.25 % in grade classification. Additionally, an app based on the optimal model was developed, enabling users to perform real-time testing and obtain results. The goal of this study was to develop the fastest and most accurate dual-label classification system, significantly improving the speed and reliability of obtaining product information during marbling selection.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107360"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel application of ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry for the rapid quantification of acrylamide in coffee 超高效液相色谱串联三重四极杆质谱法在快速定量咖啡中丙烯酰胺中的新应用

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107357

Peng Wang , Nianhua Zhang , Zhengyan Hu , Pinggu Wu , Jiawen Mao , Jingshun Zhang , Zengxuan Cai , Zhiyuan Wang , Junlin Wang

{"title":"A novel application of ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry for the rapid quantification of acrylamide in coffee","authors":"Peng Wang , Nianhua Zhang , Zhengyan Hu , Pinggu Wu , Jiawen Mao , Jingshun Zhang , Zengxuan Cai , Zhiyuan Wang , Junlin Wang","doi":"10.1016/j.jfca.2025.107357","DOIUrl":"10.1016/j.jfca.2025.107357","url":null,"abstract":"<div><div>We developed a simple, rapid, accurate, and sensitive method for the determination of acrylamide (AA) in coffee based on ultra-performance liquid chromatography tandem triple quadrupole mass spectrometry (UPLC-MS/MS). The AA in coffee was simply extracted with water and then defatted with dichloromethane. After that, concentration and purification were achieved through solid-phase extraction techniques. The calibration curve for AA showed excellent linearity over the concentration range of 0.5–500 ng/mL, with a correlation coefficient (<em>r</em>) in excess of 0.999. Furthermore, our method exhibited high precision, as evidenced by intra- and inter-day coefficients of variation of less than 5.7 % and 6.3 %, respectively. The analytical accuracy for AA quantification ranged from 98.0 % to 105.2 %, and the limit of detection and limit of quantification for AA in coffee were determined to be 1.5 μg/kg and 5.0 μg/kg, respectively. In addition, the applicability of the proposed method was assessed through its implementation in the quantification of AA in both roasted and instant coffee. The results prove that the method is simple, rapid, accurate, and sensitive, making it as an excellent option for the detection of AA in coffee.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107357"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate prediction of piperine content in black pepper using combined CNN and regression modelling with PDMAM@G electrode and cyclic voltammetry

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107355

Sanjoy Banerjee , Santanu Ghorai , Milan Dhara , Hemanta Naskar , Sk Babar Ali , Nityananda Das , Pradip Saha , Bhimsen Tudu , Arpitam Chatterjee , Rajib Bandyopadhyay , Bipan Tudu

{"title":"Accurate prediction of piperine content in black pepper using combined CNN and regression modelling with PDMAM@G electrode and cyclic voltammetry","authors":"Sanjoy Banerjee , Santanu Ghorai , Milan Dhara , Hemanta Naskar , Sk Babar Ali , Nityananda Das , Pradip Saha , Bhimsen Tudu , Arpitam Chatterjee , Rajib Bandyopadhyay , Bipan Tudu","doi":"10.1016/j.jfca.2025.107355","DOIUrl":"10.1016/j.jfca.2025.107355","url":null,"abstract":"<div><div>A novel graphite electrode with molecular imprints was developed for the selective and sensitive detection of piperine in black pepper. The electrode incorporates molecularly imprinted polymer (MIP) layers synthesized using poly (N,N-dimethylacrylamide) (PDMAM) as the monomer, ethylene glycol dimethacrylate (EGDMA) as the cross-linker, and piperine as the template, enabling specific recognition and quantification of piperine. Cyclic voltammetry (CV) was employed for electrochemical measurements, and the sensor was validated on black pepper samples from four different brands, demonstrating its practical applicability. To enhance prediction accuracy, convolutional neural network (CNN)-based feature extraction was combined with regression models for the analysis of CV signals. This hybrid approach, integrating CNN-extracted features with regression techniques such as K-nearest neighbour regressor (KNNR), gradient boost regressor (GBR), and random forest regressor (RFR), exhibited significant improvements in accuracy compared to the CNN model alone. Comprehensive experimental evaluations revealed that the CNN-KNNR model achieved a mean absolute percentage error of 0.034 and an R² value of 0.9999 when compared to reference values obtained through reverse-phase high-performance liquid chromatography (RP-HPLC).</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107355"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A molecularly imprinted electrochemical sensor based on rGO@rGNR modification for zearalenone determination

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107361

Xiaoqi Zheng, Xuan Yang, Hao Xie, Yuan Li, Xinyi Li, Binbin Zhou

{"title":"A molecularly imprinted electrochemical sensor based on rGO@rGNR modification for zearalenone determination","authors":"Xiaoqi Zheng, Xuan Yang, Hao Xie, Yuan Li, Xinyi Li, Binbin Zhou","doi":"10.1016/j.jfca.2025.107361","DOIUrl":"10.1016/j.jfca.2025.107361","url":null,"abstract":"<div><div>The research on electrochemical sensors has made great progress in recent years, but they still face challenges in detecting trace harmful substances in complex matrices. In this comprehensive investigation, quasi-one-dimensional reduced graphene nanoribbons (rGNR) and two-dimensional reduced graphene oxide (rGO) were jointly functionalized on the surface of a glassy carbon electrode, yielding a sophisticated three-dimensional rGO@rGNR hybrid material. The intrinsic synergistic effect of the two carbon materials on the structure of rGO@rGNR improved the comparative specific surface area and the total conductivity. Subsequently, by leveraging the specificity of molecularly imprinted polymers (MIP), an electrochemical sensor has been developed to detect zearalenone (ZEA). After fine-tuning the experimental parameters, the sensor exhibited an impressive linear range of 0.5–500 ng·mL<sup>–1</sup>, a low detection limit of 0.19 ng·mL<sup>–1</sup>, and outstanding selectivity. Moreover, the recovery rate of ZEA in corn meal samples is good. Compared to previously reported sensors for ZEA detection, this sensor boasts simplicity in operation, economy in cost, exceptional sensitivity, and superior selectivity.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107361"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine

IF 4 2区农林科学

Journal of Food Composition and Analysis Pub Date : 2025-02-11 DOI: 10.1016/j.jfca.2025.107354

Peng Gao, Na Wang, Yang Lu, Jinming Liu, Guannan Wang, Rui Hou

{"title":"Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine","authors":"Peng Gao, Na Wang, Yang Lu, Jinming Liu, Guannan Wang, Rui Hou","doi":"10.1016/j.jfca.2025.107354","DOIUrl":"10.1016/j.jfca.2025.107354","url":null,"abstract":"<div><div>To achieve nondestructive identification of millet origins, near-infrared spectroscopy technology was employed to obtain the original spectral data of millet. By integrating the Parrot Optimizer (PO) with the Regularized Extreme Learning Machine (RELM), the model achieved an accuracy of 83.67 % in millet origin identification. To further enhance model performance, this study incorporated strategies such as chaotic mapping and adaptivity into PO, resulting in the Improved Parrot Optimizer (IPO). The IPO was then combined with RELM to construct the IPO-RELM model, which significantly improved the model's generalization capability and robustness. Experimental results demonstrated that the IPO-RELM model outperformed the RELM model, achieving an accuracy of 98.33 %, a precision of 98.49 %, a recall of 98.33 %, an F1 score of 98.41 %, and a Kappa coefficient of 98 %, representing respective improvements of 11.32 %, 7.92 %, 11.32 %, 9.62 %, and 13.90 % over the traditional RELM model. Furthermore, the performance of the IPO-RELM model was validated using two publicly available datasets, confirming its superiority over the conventional RELM model. Compared to the PO algorithm, the IPO algorithm exhibited enhanced global search and local optimization capabilities with faster convergence speed. The IPO-RELM model accurately and efficiently identified millet origin information, providing robust support for ensuring millet quality and safety, while also contributing to the protection of the uniqueness and market value of geographically indicated agricultural products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"141 ","pages":"Article 107354"},"PeriodicalIF":4.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0