4.7 Article

Possible Alternatives: Identifying and Quantifying Adulteration in Buffalo, Goat, and Camel Milk Using Mid-Infrared Spectroscopy Combined with Modern Statistical Machine Learning Methods

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Spectroscopy

Evaluation of the adulteration of camel milk by non-camel milk using multispectral image, fluorescence and infrared spectroscopy

Oumayma Boukria et al.

Summary: This study assessed the potential of three spectroscopic techniques (MIR, fluorescence, and MSI) to detect the adulteration level in camel milk with goat, cow, and ewe milks. The results showed that fluorescence spectroscopy was the most accurate technique, with R2p ranging from 0.63 to 0.96 and an accuracy ranging from 67% to 83%. However, no technique allowed the construction of robust PLSR and PLSDA models for the simultaneous prediction of contamination of camel milk by the three milks.

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2023)

Review Food Science & Technology

Recent Advances in the Determination of Milk Adulterants and Contaminants by Mid-Infrared Spectroscopy

Carlotta Ceniti et al.

Summary: To ensure the safety and quality of milk, new tools have been developed to detect chemical contaminants, toxins, veterinary drugs, and adulteration of milk from different species. Mid-infrared spectroscopy and Fourier-transform infrared have been commonly used technologies for rapid screening of hazardous substances and confirmation of milk authenticity. These fingerprint methods can effectively determine the characteristics of raw materials without knowing the identity of each constituent, making them potential screening methods for adulteration detection. This paper reviews the latest advances in applying mid-infrared spectroscopy for the detection and quantification of adulterants, milk dilution, pathogenic bacteria, veterinary drugs, and hazardous substances in milk.
Article Agriculture, Dairy & Animal Science

Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Saeed Shadpour et al.

Summary: This study investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data in predicting CH4 emissions in lactating cows. The results showed that using only milk yield (MY) for prediction had low accuracy, while adding other factors such as fat yield (FY) and protein yield (PY) improved the accuracy. However, using only MIRS data yielded the highest accuracy compared to other combinations.

JOURNAL OF DAIRY SCIENCE (2022)

Article Agriculture, Dairy & Animal Science

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Saeed Shadpour et al.

Summary: This study aimed to improve DMI predictions of Canadian Holstein cows using milk MIRS data with the help of artificial neural networks (ANN). Different ANN architectures were explored to predict unobserved DMI, and the robustness of developed prediction models was validated by analyzing data from dairy cows in Canada, Denmark, and the United States.

JOURNAL OF DAIRY SCIENCE (2022)

Article Agriculture, Dairy & Animal Science

Mid-infrared (MIR) spectroscopy for the detection of cow?s milk in buffalo milk

Anna Antonella Spina et al.

Summary: This study developed a quantitative analysis method using Fourier transform infrared spectroscopy (FTIR) and partial least square (PLS) regression to detect the adulteration of buffalo milk with cow milk. Samples of cow and buffalo milk from different dairy farms were collected to generate mixtures of different concentrations. The results showed that this method could effectively and rapidly detect adulteration in buffalo milk.

JOURNAL OF ANIMAL SCIENCE AND TECHNOLOGY (2022)

Article Agriculture, Dairy & Animal Science

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

M. Frizzarin et al.

Summary: The study examined numerous statistical machine learning methods for predicting traits from milk samples, finding that model averaging performed best in 6 traits, while neural networks and ridge regression outperformed in 3 traits each. Additionally, support vector machines had the highest accuracy when traits were stratified into categories. The use of modern statistical machine learning methods showed improved prediction accuracy compared to the traditional partial least squares approach.

JOURNAL OF DAIRY SCIENCE (2021)

Article Chemistry, Applied

Adulteration of cow ? s milk with buffalo ? s milk detected by an on-site carbon nanoparticles-based lateral flow immunoassay

Rajan Sharma et al.

Summary: A competitive lateral flow immunoassay using amorphous carbon nanoparticles and specific antibodies has been developed for rapid detection of cow's milk adulteration with buffalo's milk. The sensitivity of the test is detecting 5% adulteration, with applicability at milk receiving stations and for heated milk samples.

FOOD CHEMISTRY (2021)

Article Chemistry, Analytical

Potential of Fourier-transform infrared spectroscopy in adulteration detection and quality assessment in buffalo and goat milks

Sevval Sen et al.

Summary: This study successfully differentiated goat-cow and buffalo-cow milk mixtures using FTIR spectroscopic data and chemometric methods, and accurately predicted the quality parameters of the mixed milks. By utilizing partial least square models, FTIR spectroscopy was found to rapidly detect adulteration in milk and predict quality parameters regardless of the type of milk being used.

MICROCHEMICAL JOURNAL (2021)

Article Chemistry, Applied

SPECTROSCOPIC METHOD (FTIR-ATR) AND CHEMOMETRIC TOOLS TO DETECT COW'S MILK ADDITION TO BUFFALO'S MILK

L. K. R. Silva et al.

REVISTA MEXICANA DE INGENIERIA QUIMICA (2020)

Review Chemistry, Applied

A review of Fourier Transform Infrared (FTIR) spectroscopy used in food adulteration and authenticity investigations

Reema Valand et al.

FOOD ADDITIVES AND CONTAMINANTS PART A-CHEMISTRY ANALYSIS CONTROL EXPOSURE & RISK ASSESSMENT (2020)

Article Agriculture, Dairy & Animal Science

Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?

P. Delhez et al.

JOURNAL OF DAIRY SCIENCE (2020)

Article Spectroscopy

Potentiality of using front face fluorescence spectroscopy for quantitative analysis of cow milk adulteration in buffalo milk

Rahat Ullah et al.

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY (2020)

Article Agriculture, Multidisciplinary

Discrimination of milk species using Raman spectroscopy coupled withpartial least squares discriminant analysisin raw and pasteurized milk

Nazife N. Yazgan et al.

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE (2020)

Article Agriculture, Dairy & Animal Science

Detection of buffalo milk adulteration with cow milk by capillary electrophoresis analysis

Francesca Trimboli et al.

JOURNAL OF DAIRY SCIENCE (2019)

Article Food Science & Technology

Identification of milk fraud using laser-induced breakdown spectroscopy (LIBS)

Banu Sezer et al.

INTERNATIONAL DAIRY JOURNAL (2018)

Article Food Science & Technology

Quick vacuum drying of liquid samples prior to ATR-FTIR spectral collection improves the quantitative prediction: a case study of milk adulteration

Huseyin Ayvaz et al.

INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY (2018)

Article Agriculture, Dairy & Animal Science

Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows

J. R. R. Dorea et al.

JOURNAL OF DAIRY SCIENCE (2018)

Review Chemistry, Multidisciplinary

Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials

Marena Manley

CHEMICAL SOCIETY REVIEWS (2014)

Review Agriculture, Dairy & Animal Science

Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits

M. De Marchi et al.

JOURNAL OF DAIRY SCIENCE (2014)

Article Agriculture, Dairy & Animal Science

Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

H. Soyeurt et al.

JOURNAL OF DAIRY SCIENCE (2011)

Article Food Science & Technology

Defining the Public Health Threat of Food Fraud

John Spink et al.

JOURNAL OF FOOD SCIENCE (2011)

Review Medicine, General & Internal

Diagnosing and Managing Common Food Allergies A Systematic Review

Jennifer J. Schneider Chafen et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2010)

Article Agriculture, Dairy & Animal Science

Influence of fatty acid chain length and unsaturation on mid-infrared milk analysis

K. E. Kaylegian et al.

JOURNAL OF DAIRY SCIENCE (2009)