Dairy Safety Prediction Based on Machine Learning Combined with Chemicals.

BACKGROUND Dairy safety has caused widespread concern in society. Unsafe dairy products have threatened people's health and lives. In order to improve the safety of dairy products and effectively prevent the occurrence of dairy insecurity, countries have established different prevention and control measures and safety warnings. OBJECTIVE The purpose of this study is to establish a dairy safety prediction model based on machine learning to determine whether the dairy products are qualified. METHOD The 34 common items in the dairy sampling inspection were used as features in this study. Feature selection was performed on the data to obtain a better subset of features, and different algorithms were applied to construct the classification model. RESULTS The results show that the prediction model constructed by using a subset of features including "total plate", "water" and "nitrate" is superior. The SN, SP and ACC of the model were 62.50%, 91.67% and 72.22%, respectively. And it was found that the accuracy of the model established by the integrated algorithm is higher than that by the non-integrated algorithm. CONCLUSION This study provides a new method for assessing dairy safety. It helps to improve the quality of dairy products, ensure the safety of dairy products, and reduce the risk of dairy safety.

[1]  Yu Jiang,et al.  mRMR-based feature selection for classification of cotton foreign matter using hyperspectral imaging , 2015, Comput. Electron. Agric..

[2]  Deng Limiao,et al.  Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data , 2018, Comput. Electron. Agric..

[3]  Zhonggai Zhao,et al.  mRMR-based wavelength selection for quantitative detection of Chinese yellow wine using NIRS , 2018 .

[4]  Xiaohui Du,et al.  Leukocyte recognition in human fecal samples using texture features. , 2018, Journal of the Optical Society of America. A, Optics, image science, and vision.

[5]  Wei Chen,et al.  Predicting peroxidase subcellular location by hybridizing different descriptors of Chou' pseudo amino acid patterns. , 2014, Analytical biochemistry.

[6]  Yoshio Makino,et al.  Rapid and non-destructive detection of chicken adulteration in minced beef using visible near-infrared hyperspectral imaging and machine learning , 2016 .

[7]  Zhaohui Qi,et al.  Recent Progress in Long Noncoding RNAs Prediction , 2017, Current Bioinformatics.

[8]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[9]  E. Caldas,et al.  Preliminary Quantitative Microbial Risk Assessment for Staphylococcus enterotoxins in fresh Minas cheese, a popular food in Brazil , 2017 .

[10]  Ning Wang,et al.  AdaBoost classifiers for pecan defect classification , 2011 .

[11]  Rong Chen,et al.  HBPred: a tool to identify growth hormone-binding proteins , 2018, International journal of biological sciences.

[12]  Efstathios Z. Panagou,et al.  Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines , 2016 .

[13]  Claudia Gonzalez Viejo,et al.  Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications , 2018, Food Control.

[14]  Chidchanok Lursinsap,et al.  An Efficient Prediction of HPV Genotypes from Partial Coding Sequences by Chaos Game Representation and Fuzzy k-Nearest Neighbor Technique , 2017 .

[15]  Prakash P. Shenoy,et al.  An adaptive heuristic for feature selection based on complementarity , 2018, Machine Learning.

[16]  Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis , 2018, Food Analytical Methods.

[17]  Stephanie L. H. Defibaugh-Chávez,et al.  Using a Six Sigma Fishbone Analysis Approach To Evaluate the Effect of Extreme Weather Events on Salmonella Positives in Young Chicken Slaughter Establishments. , 2016, Journal of food protection.

[18]  Thomas Brendan Murphy,et al.  Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications. , 2010, The annals of applied statistics.

[19]  Mohammed Bennamoun,et al.  ECMSRC: A Sparse Learning Approach for the Prediction of Extracellular Matrix Proteins , 2017 .

[20]  Farm to consumption risk assessment for Staphylococcus aureus and staphylococcal enterotoxins in fluid milk in China , 2016 .

[21]  Larry J. Eshelman,et al.  A dynamic ensemble approach to robust classification in the presence of missing data , 2015, Machine Learning.

[22]  P. Grandjean,et al.  Neurological and neuropsychological functions in adults with a history of developmental arsenic poisoning from contaminated milk powder. , 2016, Neurotoxicology and teratology.

[23]  Bassam Al-Salemi,et al.  LDA-AdaBoost.MH: Accelerated AdaBoost.MH based on latent Dirichlet allocation for text categorization , 2015, J. Inf. Sci..

[24]  Jiewen Zhao,et al.  Non-destructively sensing pork’s freshness indicator using near infrared multispectral imaging technique , 2015 .

[25]  E. Fèvre,et al.  Mapping Nairobi's dairy food system: An essential analysis for policy, industry and research , 2018, Agricultural systems.

[26]  Stephen A. Billings,et al.  A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking , 2017, Pattern Recognit..

[27]  Zhiqiang Geng,et al.  Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety , 2017 .

[28]  Wen Li,et al.  Identification and Analysis of cancer diagnosis using probabilistic classification vector machines with feature selection , 2017 .

[29]  Hao Lv,et al.  Identify origin of replication in Saccharomyces cerevisiae using two-step feature selection technique , 2018, Bioinform..

[30]  Lei Yang,et al.  Discrimination of membrane transporter protein types using K-nearest neighbor method derived from the similarity distance of total diversity measure. , 2015, Molecular bioSystems.

[31]  K. Kannan,et al.  Occurrence and exposure assessment of perchlorate, iodide and nitrate ions from dairy milk and water in Japan and Sri Lanka. , 2011, Journal of environmental monitoring : JEM.

[32]  Balasubramanian Raman,et al.  Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval , 2018, Multimedia Tools and Applications.

[33]  Guangpeng Li,et al.  PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition , 2017, Bioinform..

[34]  Abhishek Bhola and Shailendra Singh,et al.  Gene Selection Using High Dimensional Gene Expression Data: An Appraisal , 2016 .

[35]  Md. Nurul Haque Mollah,et al.  A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis , 2018, Current Bioinformatics.

[36]  Zijiang Yang,et al.  A novel feature selection method to predict protein structural class , 2018, Comput. Biol. Chem..

[37]  OEzkan Akin,et al.  Benchmarking Classification Models for Cell Viability on Novel Cancer Image Datasets , 2019, Current Bioinformatics.

[38]  Zhen Xu,et al.  Maximum relevance, minimum redundancy band selection based on neighborhood rough set for hyperspectral data classification , 2016 .

[39]  Kurt K. Klein,et al.  Melamine in milk products in China: Examining the factors that led to deliberate use of the contaminant , 2010 .

[40]  Xuli Wu,et al.  Challenges to improve the safety of dairy products in China , 2018, Trends in Food Science & Technology.

[41]  Sivaraj Rajappan and DeviPriya Rangasamy.  Adaptive Genetic Algorithm with Exploration-Exploitation Tradeoff for Preprocessing Microarray Datasets , 2017 .

[42]  H. Ayvaz,et al.  Quick vacuum drying of liquid samples prior to ATR-FTIR spectral collection improves the quantitative prediction: a case study of milk adulteration , 2018, International Journal of Food Science & Technology.

[43]  Yan Lin,et al.  iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators , 2018, Bioinform..

[44]  Guo-Zheng Li,et al.  Using AdaBoost for the prediction of subcellular location of prokaryotic and eukaryotic proteins , 2008, Molecular Diversity.

[45]  Wei Chen,et al.  i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome , 2019, Bioinform..

[46]  Quan Zou.  Latest Machine Learning Techniques for Biomedicine and Bioinformatics , 2019 .

[47]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Jincheng Li,et al.  Feature Extractions for Computationally Predicting Protein Post- Translational Modifications , 2017, Current Bioinformatics.

[49]  Chu Zhang,et al.  Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging , 2018, Molecules.

[50]  Edward Sazonov,et al.  Accelerometer-Based Detection of Food Intake in Free-Living Individuals , 2018, IEEE Sensors Journal.

[51]  Qin Chen,et al.  2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors , 2017, Molecular Diversity.

[52]  Yan He,et al.  Classification of Small GTPases with Hybrid Protein Features and Advanced Machine Learning Techniques , 2017, Current Bioinformatics.

[53]  Trevor Hastie,et al.  Multi-class AdaBoost ∗ , 2009 .

[54]  G. Miller,et al.  Major scientific advances with dairy foods in nutrition and health. , 2006, Journal of dairy science.

[55]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[56]  Subrahmanyam Murala,et al.  Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval , 2015, Neurocomputing.

[57]  Wei Chen,et al.  iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites in Homo sapiens , 2018, J. Comput. Biol..

[58]  Peng Chen,et al.  Early Stage Identification of Alzheimer’s Disease Using a Two-stage Ensemble Classifier , 2018, Current Bioinformatics.

[59]  A. Velthuis,et al.  Process audits versus product quality monitoring of bulk milk. , 2011, Journal of dairy science.