Determination of the lactose content in low-lactose milk using Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network

Ribeiro, Daniela Cotta; Neto, Habib Asseiss; Lima, J. D.; Assis, Débora Cristina Sampaio de; Keller, Kelly Moura; Campos, Sérgio Vale Aguiar; Oliveira, Daniel A.; Fonseca, L. R. C.

doi:10.1016/j.heliyon.2023.e12898

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The demand for low-lactose products, including milk, has become widespread due to the high number of consumers with low lactase enzyme production and, thus, with lactose intolerance. Ribeiro and colleagues [48] applied the association of FTIR with machine learning tools as an original proposal to detect and quantify residual lactose and other sugars in low-lactose milk. The results of this interesting study performed on raw milk, pasteurized milk, and ultra-high temperature (UHT) milk indicated good accuracy (95%) for classification.…”

Section: Adulterants Diluents Chemical Substances and Mycotoxins In Milkmentioning

confidence: 99%

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

Ceniti,

Spina,

Piras

et al. 2023

Foods

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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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Section: Adulterants Diluents Chemical Substances and Mycotoxins In Milkmentioning

confidence: 99%

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

Ceniti,

Spina,

Piras

et al. 2023

Foods

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“…Various approaches have been developed to detect LAC levels using different analytical techniques. These techniques include high resolution ultrasonic spectroscopy [7], Fouriertransform infrared spectroscopy (FTIR) [8], FT-mid infrared (FT-MIR) spectral imaging [9], mass spectrometry [10], Raman spectroscopy [11], high-performance liquid chromatography (HPLC) [12], capillary electrophoresis [13], optical [14], and electrochemical sensors [15]. Electrochemical sensors are especially remarkable due to their excellent sensitivity, selectivity, and stability when compared to other methods.…”

Section: Introductionmentioning

confidence: 99%

Highly sensitive rhodamine B dye-based electrochemical sensor for lactose detection

Yıldız,

Taşaltın,

Unal

et al. 2024

Phys. Scr.

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Lactose (LAC) is the main disaccharide sugar found in milk and dairy products, making it easily accessible as a food source. In addition to its importance for human and animal health, LAC concentrations serve as a biomarker of milk quality. In this study, we reported the detection of LAC using a highly sensitive electrochemical sensor based on rhodamine B (RhB) dye. The prepared RhB-based sensor exhibited excellent characteristics, including a straightforward manufacturing technique, cost-effectiveness, exceptional sensitivity, and selectivity. We conducted a comparative analysis of the electrochemical response of the novel RhB-based sensor, evaluating its electrochemical performance across varying LAC concentrations and when different analyte materials (glucose, fructose, and maltose) were employed. The proposed analytical method was utilized to determine LAC using an electrochemical RhB-based sensor under optimal experimental conditions. As a result, it was experimentally observed that high sensor sensitivity of RhB based sensor against LAC was inversely proportional to the impedance value and directly proportional to conductance which corolated with the transport mechanism of RhB.

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“…With the ongoing advancements in deep learning technology, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have risen as two of the most influential neural network architectures [ 21 ]. With the use of hyperparameter adjustment and a saliency map, CNN models may be constructed directly from infrared spectra [ 22 ]. Text classification can be accomplished with the help of machine learning and deep learning methods like Bi-LSTM and CNN models [ 23 ], Coordinated CNN-LSTM-Attention (CCLA) models [ 24 ], and models for Regional Tree-Structured CNN-LSTM [ 25 ], for text sentiment classification and dimensional sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

Das,

Islam,

Hasan

et al. 2023

Heliyon

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Determination of the lactose content in low-lactose milk using Fourier-transform infrared spectroscopy (FTIR) and convolutional neural network

Cited by 11 publications

References 18 publications

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

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

Highly sensitive rhodamine B dye-based electrochemical sensor for lactose detection

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models

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