Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

Vasafi, Pegah Sadeghi; Hinrichs, Jörg; Hitzmann, Bernd

doi:10.1016/j.foodcont.2021.108442

Cited by 10 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose Gaussian process regression (GPR), a nonparametric regression model, because GPR has been successfully used in material science, chemistry, bacteria identifications, and milk processing quality control. [49][50][51][52][53] We established the GPR models using the SERS spectra from a single boronic acid (4MBA or 1TBA) and the collective spectra. The mole fractions of sialic acids spiked in the background lactose range from 0 to 2 mol%.…”

Section: Quantification Of Sialic Acidsmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.

show abstract

Section: Quantification Of Sialic Acidsmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Advanced Sensor Research

View full text Add to dashboard Cite

show abstract

“…The spectra were processed by DAPC, and the canonical variables were used to build a machine learning regression model. We chose Gaussian process regression (GPR), a nonparametric regression model, because GPR has been successfully used in material science, chemistry, bacteria identifications, and milk processing quality control [47][48][49][50][51] . We established the GPR models using the SERS spectra from a single boronic acid (4MBA or 1TBA) and the collective spectra.…”

Section: Quantification Of Sialic Acidsmentioning

confidence: 99%

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Kuai

Park

et al. 2023

Preprint

View full text Add to dashboard Cite

Glycans are the most abundant and diverse fundamental biomolecules. Profiling glycans is essential to elucidate biomolecular mechanisms and to predict biological consequences; however, the analysis of glycans remains challenging due to their structural complexity. A novel glycan detection platform is established through the integration of surface-enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. SERS is able to distinguish isomeric structures and provide fingerprint information of molecules. Boronic acid receptors can selectively bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. For proof-of-concept, two boronic acids, 4-mercaptophenylboronic acid (4MBA) and 1-thianthrenylboronic acid (1TBA) were used for glycan detection. The spectra were analyzed by the machine learning algorithm. The sensing platform successfully recognized the stereoisomers (glucose, mannose, and galactose), the structural isomers with α-1,4 and α-1,6 glycosidic linkages, and the β-1,4 glycosidic linkage within lactose molecules. The collective spectra that combine the spectra from both boronic acid receptors could improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition to qualitative analysis, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. The quantification accuracy reached a coefficient of determination (R2) value of 0.998 and a normalized mean square error (NMSE) of 0.00195. This low-cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent glycan screening in standard biological laboratories.

show abstract

“…The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value [14]. Using Raman spectroscopy Vasafi et al [15] could demonstrate that Gaussian process regression as well as autoencoder were able to distinguish between reference milk and manipulated milk, but could not identify which manipulation took place.…”

Section: Practical Applicationmentioning

confidence: 99%

“…Using Raman spectroscopy Vasafi et al. [ 15 ] could demonstrate that Gaussian process regression as well as autoencoder were able to distinguish between reference milk and manipulated milk, but could not identify which manipulation took place.…”

Section: Introductionmentioning

confidence: 99%

Comparison of various classification techniques for supervision of milk processing

Vasafi

Hitzmann

2021

Engineering in Life Sciences

Self Cite

View full text Add to dashboard Cite

Detecting the types of anomalies that can occur throughout the milk processing process is an important task since it can assist providers in maintaining control over the process. The Raman spectrometer was used in conjunction with several classification approaches-linear discriminant analysis, decision tree, support vector machine, and k nearest neighbor-to establish a viable method for detecting different types of anomalies that may occur during the process-temperature and fat variation and added water or cleaning solution. Milk with 5% fat measured at 10 • C was used as the reference milk for this study. Added water, cleaning solution, milk with various fat contents and different temperatures were used to detect abnormal conditions. While decision trees and linear discriminant analysis were unable to accurately categorize the various type of anomalies, the k nearest neighbor and support vector machine provided promising results. The accuracy of the support vector machine test set and the k nearest neighbor test set were 81.4% and 84.8%, respectively. As a result, it is reasonable to conclude that both algorithms are capable of appropriately classifying the various groups of samples. It can assist milk industries in determining what is wrong during milk processing.

show abstract

Establishing a novel procedure to detect deviations from standard milk processing by using online Raman spectroscopy

Cited by 10 publications

References 26 publications

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms

Comparison of various classification techniques for supervision of milk processing

Contact Info

Product

Resources

About