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Spina AA, Ceniti C, De Fazio R, Oppedisano F, Palma E, Gugliandolo E, Crupi R, Raza SHA, Britti D, Piras C, Morittu VM. Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds. Animals (Basel) 2024; 14:1271. [PMID: 38731274 PMCID: PMC11083570 DOI: 10.3390/ani14091271] [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: 02/29/2024] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.
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Affiliation(s)
- Anna Antonella Spina
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Carlotta Ceniti
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Rosario De Fazio
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
| | - Francesca Oppedisano
- Department of Health Sciences, Institute of Research for Food Safety & Health (IRC-FSH), “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Ernesto Palma
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Department of Health Sciences, Institute of Research for Food Safety & Health (IRC-FSH), “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Nutramed S.c.a.r.l., Complesso Ninì Barbieri, Roccelletta di Borgia, 88021 Catanzaro, Italy
| | - Enrico Gugliandolo
- Department of Veterinary Science, University of Messina, 98166 Messina, Italy; (E.G.); (R.C.)
| | - Rosalia Crupi
- Department of Veterinary Science, University of Messina, 98166 Messina, Italy; (E.G.); (R.C.)
| | - Sayed Haidar Abbas Raza
- Guangdong Provincial Key Laboratory of Food Quality and Safety, Nation-Local Joint Engineering Research Center for Machining and Safety of Livestock and Poultry Products, South China Agricultural University, Guangzhou 510642, China;
| | - Domenico Britti
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Cristian Piras
- Department of Health Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy; (A.A.S.); (C.C.); (R.D.F.); (E.P.); (D.B.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
| | - Valeria Maria Morittu
- Interdepartmental Center Veterinary Service for Human and Animal Health, “Magna Græcia University” of Catanzaro, CISVetSUA, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy;
- Department of Medical and Surgical Sciences, “Magna Græcia University” of Catanzaro, Campus Universitario “Salvatore Venuta” Viale Europa, 88100 Catanzaro, Italy
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Babatunde HA, Collins J, Lukman R, Saxton R, Andersen T, McDougal OM. SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR. Foods 2024; 13:166. [PMID: 38201194 PMCID: PMC10778881 DOI: 10.3390/foods13010166] [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: 11/27/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.
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Affiliation(s)
| | - Joseph Collins
- Biomolecular Sciences Graduate Program, Boise State University, Boise, ID 83725, USA;
| | - Rianat Lukman
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | - Rose Saxton
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
| | | | - Owen M. McDougal
- Department of Chemistry and Biochemistry, Boise State University, Boise, ID 83725, USA; (R.L.); (R.S.)
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3
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Ceniti C, Spina AA, Piras C, Oppedisano F, Tilocca B, Roncada P, Britti D, Morittu VM. Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy. Foods 2023; 12:2917. [PMID: 37569186 PMCID: PMC10418805 DOI: 10.3390/foods12152917] [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: 07/04/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
The presence of chemical contaminants, toxins, or veterinary drugs in milk, as well as the adulteration of milk from different species, has driven the development of new tools to ensure safety and quality. Several analytical procedures have been proposed for the rapid screening of hazardous substances or the selective confirmation of the authenticity of milk. Mid-infrared spectroscopy and Fourier-transform infrared have been two of the most relevant technologies conventionally employed in the dairy industry. These fingerprint methodologies can be very powerful in determining the trait of raw material without knowing the identity of each constituent, and several aspects suggest their potential as a screening method to detect adulteration. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, the presence of pathogenic bacteria, veterinary drugs, and hazardous substances in milk.
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Affiliation(s)
- Carlotta Ceniti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Anna Antonella Spina
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Cristian Piras
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Francesca Oppedisano
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Bruno Tilocca
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Paola Roncada
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
| | - Domenico Britti
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
| | - Valeria Maria Morittu
- Department of Health Sciences, University of Catanzaro Magna Græcia, 88100 Catanzaro, Italy; (C.C.); (A.A.S.); (F.O.); (B.T.); (P.R.); (D.B.); (V.M.M.)
- Interdepartmental Center Veterinary Service for Human and Animal Health, University of Catanzaro Magna Græcia, CISVetSUA, 88100 Catanzaro, Italy
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4
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Wu LY, Liu FM, Weng SS, Lin WC. EL V.2 Model for Predicting Food Safety Risks at Taiwan Border Using the Voting-Based Ensemble Method. Foods 2023; 12:foods12112118. [PMID: 37297360 DOI: 10.3390/foods12112118] [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: 04/25/2023] [Revised: 05/17/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the first-generation ensemble learning prediction model (EL V.1) was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model (EL V.2) was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. In this study, Elastic Net was used to select the characteristic risk factors. Two algorithms were used to construct the new model: The Bagging-Gradient Boosting Machine and Bagging-Elastic Net. In addition, Fβ was used to flexibly control the sampling rate, improving the predictive performance and robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020-2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p < 0.001) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.
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Affiliation(s)
- Li-Ya Wu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Fang-Ming Liu
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
| | - Sung-Shun Weng
- Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan
| | - Wen-Chou Lin
- Food and Drug Administration, Ministry of Welfare, Taipei 115209, Taiwan
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5
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Iida D, Kokawa M, Kitamura Y. Estimation of Apple Mealiness by Means of Laser Scattering Measurement. FOOD BIOPROCESS TECH 2023. [DOI: 10.1007/s11947-023-03068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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6
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Walleser E, Reyes JFM, Anklam K, Pralle RS, White HM, Unger S, Panne N, Kammer M, Plattner S, Döpfer D. Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy. Prev Vet Med 2023; 213:105860. [PMID: 36724618 PMCID: PMC10038899 DOI: 10.1016/j.prevetmed.2023.105860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/25/2022] [Accepted: 01/23/2023] [Indexed: 01/26/2023]
Abstract
Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets.
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Affiliation(s)
- E Walleser
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison 53706, USA.
| | - J F Mandujano Reyes
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison 53706, USA
| | - K Anklam
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison 53706, USA
| | - R S Pralle
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706, USA; School of Agriculture, University of Wisconsin-Platteville, Platteville, WI 53818, USA
| | - H M White
- Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706, USA
| | - S Unger
- Milchprüfring Bayern e. V. (Bavarian Association for Raw Milk Testing), 85283 Wolnzach, Germany
| | - N Panne
- Milchprüfring Bayern e. V. (Bavarian Association for Raw Milk Testing), 85283 Wolnzach, Germany
| | - M Kammer
- Milchprüfring Bayern e. V. (Bavarian Association for Raw Milk Testing), 85283 Wolnzach, Germany
| | - S Plattner
- Milchprüfring Bayern e. V. (Bavarian Association for Raw Milk Testing), 85283 Wolnzach, Germany; LKV Bayern e. V. (Dairy Herd Improvement Association of Bavaria), 80687 Munich, Germany
| | - D Döpfer
- University of Wisconsin - Madison, School of Veterinary Medicine, Department of Medical Science, Veterinary Medicine Bldg., 2015 Linden Dr, Madison 53706, USA
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Chung T, Tam IYS, Lam NYY, Yang Y, Liu B, He B, Li W, Xu J, Yang Z, Zhang L, Cao JN, Lau LT. Non-targeted detection of food adulteration using an ensemble machine-learning model. Sci Rep 2022; 12:20956. [PMID: 36470940 PMCID: PMC9722920 DOI: 10.1038/s41598-022-25452-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Recurrent incidents of economically motivated adulteration have long-lasting and devastating effects on public health, economy, and society. With the current food authentication methods being target-oriented, the lack of an effective methodology to detect unencountered adulterants can lead to the next melamine-like outbreak. In this study, an ensemble machine-learning model that can help detect unprecedented adulteration without looking for specific substances, that is, in a non-targeted approach, is proposed. Using raw milk as an example, the proposed model achieved an accuracy and F1 score of 0.9924 and 0. 0.9913, respectively, when the same type of adulterants was presented in the training data. Cross-validation with spiked contaminants not routinely tested in the food industry and blinded from the training data provided an F1 score of 0.8657. This is the first study that demonstrates the feasibility of non-targeted detection with no a priori knowledge of the presence of certain adulterants using data from standard industrial testing as input. By uncovering discriminative profiling patterns, the ensemble machine-learning model can monitor and flag suspicious samples; this technique can potentially be extended to other food commodities and thus become an important contributor to public food safety.
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Affiliation(s)
- Teresa Chung
- grid.16890.360000 0004 1764 6123Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Issan Yee San Tam
- grid.16890.360000 0004 1764 6123Research and Innovation Office, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Nelly Yan Yan Lam
- grid.221309.b0000 0004 1764 5980Institute for Innovation, Translation and Policy Research, Hong Kong Baptist University, Kowloon Tong, Hong Kong China ,Food Safety Consortium, Hong Kong, China
| | - Yanni Yang
- grid.16890.360000 0004 1764 6123Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Boyang Liu
- Inner Mongolia Mengniu Dairy (Group) Co., Ltd, Hohhot, China
| | - Billy He
- grid.16890.360000 0004 1764 6123Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Wengen Li
- grid.16890.360000 0004 1764 6123Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Jie Xu
- Danone Open Science Research Center, Shanghai, China
| | - Zhigang Yang
- Inner Mongolia Mengniu Dairy (Group) Co., Ltd, Hohhot, China
| | - Lei Zhang
- grid.16890.360000 0004 1764 6123Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Jian Nong Cao
- grid.16890.360000 0004 1764 6123Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China
| | - Lok-Ting Lau
- grid.16890.360000 0004 1764 6123Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong China ,grid.221309.b0000 0004 1764 5980Institute for Innovation, Translation and Policy Research, Hong Kong Baptist University, Kowloon Tong, Hong Kong China ,Food Safety Consortium, Hong Kong, China ,grid.221309.b0000 0004 1764 5980School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong China
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Ma P, Zhang Z, Jia X, Peng X, Zhang Z, Tarwa K, Wei CI, Liu F, Wang Q. Neural network in food analytics. Crit Rev Food Sci Nutr 2022; 64:4059-4077. [PMID: 36322538 DOI: 10.1080/10408398.2022.2139217] [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] [Indexed: 06/16/2023]
Abstract
Neural network (i.e. deep learning, NN)-based data analysis techniques have been listed as a pivotal opportunity to protect the integrity and safety of the global food supply chain and forecast $11.2 billion in agriculture markets. As a general-purpose data analytic tool, NN has been applied in several areas of food science, such as food recognition, food supply chain security and omics analysis, and so on. Therefore, given the rapid emergence of NN applications in food safety, this review aims to provide a comprehensive overview of the NN application in food analysis for the first time, focusing on domain-specific applications in food analysis by introducing fundamental methodology, reviewing recent and notable progress, and discussing challenges and potential pitfalls. NN demonstrated that it has a bright future through effective collaboration between food specialist and the broader community in the food field, for example, superiority in food recognition, sensory evaluation, pattern recognition of spectroscopy and chromatography. However, major challenges impeded NN extension including void in the food scientist-friendly interface software package, incomprehensible model behavior, multi-source heterogeneous data, and so on. The breakthrough from other fields proved NN has the potential to offer a revolution in the immediate future.
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Affiliation(s)
- Peihua Ma
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Zhikun Zhang
- CISPA Helmholtz Center for Information Security, Saarbrucken, Germany
| | - Xiaoxue Jia
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Xiaoke Peng
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Zhi Zhang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Kevin Tarwa
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Cheng-I Wei
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
| | - Fuguo Liu
- College of Food Science and Engineering, Northwest A&F University, Yangling, Shaanxi, PR China
| | - Qin Wang
- Department of Nutrition and Food Science, College of Agriculture and Natural Resources, University of Maryland, College Park, Maryland, USA
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Lima JS, Ribeiro DC, Neto HA, Campos SV, Leite MO, Fortini MEDR, de Carvalho BPM, Almeida MVO, Fonseca LM. A machine learning proposal method to detect milk tainted with cheese whey. J Dairy Sci 2022; 105:9496-9508. [DOI: 10.3168/jds.2021-21380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 06/25/2022] [Indexed: 11/07/2022]
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10
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Chakraborty SK, Chandel NS, Jat D, Tiwari MK, Rajwade YA, Subeesh A. Deep learning approaches and interventions for futuristic engineering in agriculture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07744-x] [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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11
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Kong D, Shi Y, Sun D, Zhou L, Zhang W, Qiu R, He Y. Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Shine P, Murphy MD. Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:52. [PMID: 35009593 PMCID: PMC8747441 DOI: 10.3390/s22010052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 05/06/2023]
Abstract
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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Affiliation(s)
| | - Michael D. Murphy
- Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland;
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13
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Amsaraj R, Ambade ND, Mutturi S. Variable selection coupled to PLS2, ANN and SVM for simultaneous detection of multiple adulterants in milk using spectral data. Int Dairy J 2021. [DOI: 10.1016/j.idairyj.2021.105172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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14
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Abstract
Ensemble learning was adopted to design risk prediction models with the aim of improving border inspection methods for food imported into Taiwan. Specifically, we constructed a set of prediction models to enhance the hit rate of non-conforming products, thus strengthening the border control of food products to safeguard public health. Using five algorithms, we developed models to provide recommendations for the risk assessment of each imported food batch. The models were evaluated by constructing a confusion matrix to calculate predictive performance indicators, including the positive prediction value (PPV), recall, harmonic mean of PPV and recall (F1 score), and area under the curve. Our results showed that ensemble learning achieved better and more stable prediction results than any single algorithm. When the results of comparable data periods were examined, the non-conformity hit rate was found to increase significantly after online implementation of the ensemble learning models, indicating that ensemble learning was effective at risk prediction. In addition to enhancing the inspection hit rate of non-conforming food, the results of this study can serve as a reference for the improvement of existing random inspection methods, thus strengthening capabilities in food risk management.
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15
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Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.04.008] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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16
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Spieß L, de Peinder P, van den Bijgaart H. Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms. Foods 2021; 10:1111. [PMID: 34069770 PMCID: PMC8157011 DOI: 10.3390/foods10051111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 11/16/2022] Open
Abstract
Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.
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Affiliation(s)
- Lukas Spieß
- Qlip B.V., P.O. Box 119, 7200 AC Zutphen, The Netherlands;
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17
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Mota LFM, Pegolo S, Baba T, Peñagaricano F, Morota G, Bittante G, Cecchinato A. Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. J Dairy Sci 2021; 104:8107-8121. [PMID: 33865589 DOI: 10.3168/jds.2020-19861] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 03/05/2021] [Indexed: 12/11/2022]
Abstract
Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.
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Affiliation(s)
- Lucio F M Mota
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - Sara Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy.
| | - Toshimi Baba
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | | | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Viale dell' Università 16, 35020 Legnaro, Italy
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18
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Epistasis Analysis: Classification Through Machine Learning Methods. Methods Mol Biol 2021. [PMID: 33733366 DOI: 10.1007/978-1-0716-0947-7_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.
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19
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Lin J. Deep Neural Networks for Epistatic Sequence Analysis. Methods Mol Biol 2021; 2212:277-289. [PMID: 33733362 DOI: 10.1007/978-1-0716-0947-7_17] [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] [Indexed: 06/12/2023]
Abstract
We report a step-by-step protocol to use pysster, a TensorFlow-based package for building deep neural networks on a broad range of epistatic sequences such as DNA, RNA, or annotated secondary structure sequences. Pysster provides users comprehensive supports for developing, training, and evaluating the self-defined deep neural networks on sequence data. Moreover, pysster allows users to easily visualize the resulting perditions, which is helpful to uncover the "black box" of deep neural networks. Here, we describe a step-by-step application of pysster to classify the RNA A-to-I editing regions and interpret the model predictions. To further demonstrate the generalizability of pysster, we utilized it to build and evaluated a new deep neural network on an artificial epistatic sequence dataset.
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Affiliation(s)
- Jiecong Lin
- City University of Hong Kong, Kowloon Tong, Hong Kong.
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20
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Liang N, Sun S, Zhang C, He Y, Qiu Z. Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food. Crit Rev Food Sci Nutr 2020; 62:2963-2984. [PMID: 33345592 DOI: 10.1080/10408398.2020.1862045] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The authentication and traceability of food attract more attention due to the increasing consumer awareness regarding nutrition and health, being a new hotspot of food science. Infrared spectroscopy (IRS) combined with shallow neural network has been widely proven to be an effective food analysis technology. As an advanced deep learning technology, deep neural network has also been explored to analyze and solve food-related IRS problems in recent years. The present review begins with brief introductions to IRS and artificial neural network (ANN), including shallow neural network and deep neural network. More notably, it emphasizes the comprehensive overview of the advances of the technology combined IRS with ANN for the authentication and traceability of food, based on relevant literature from 2014 to early 2020. In detail, the types of IRS and ANN, modeling processes, experimental results, and model comparisons in related studies are described to set forth the usage and performance of the combined technology for food analysis. The combined technology shows excellent ability to authenticate food quality and safety, involving chemical components, freshness, microorganisms, damages, toxic substances, and adulteration. As well, it shows excellent performance in the traceability of food variety and origin. The advantages, current limitations, and future trends of the combined technology are further discussed to provide a thoughtful viewpoint on the challenges and expectations of online applications for the authentication and traceability of food.
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Affiliation(s)
- Ning Liang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Sashuang Sun
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Chu Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.,Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China
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21
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Application of machine learning to improve dairy farm management: A systematic literature review. Prev Vet Med 2020; 187:105237. [PMID: 33418514 DOI: 10.1016/j.prevetmed.2020.105237] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/20/2020] [Accepted: 12/13/2020] [Indexed: 11/22/2022]
Abstract
In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.
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