Rapid detection of the presence, activity and concentration of microbial transglutaminase in yogurt using infrared spectroscopy combined with chemometrics

IF 6.8 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Innovative Food Science & Emerging Technologies Pub Date : 2025-09-10 DOI:10.1016/j.ifset.2025.104225

Hatice Sıçramaz , Huseyin Ayvaz , Ahmed Menevseoglu , Mysa Ahmed Hasan Ayash Yaaqob , Muhammed Ali Dogan , Mustafa Ozturk

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引用次数: 0

Abstract

The goal of this study was to develop a rapid method by using near-infrared (NIR) diffuse reflectance and mid-infrared (MIR) spectroscopy to detect the use, status (active or inactive), and concentration of microbial transglutaminase (mTGase) in yogurt. Control samples were manufactured without mTGase. Two different levels of mTGase concentration were employed: 1 and 2 units. Half of the enzyme-added samples were inactivated after yogurt manufacture to detect the active/inactive status of mTGase. Both for NIR and MIR, analyzed via the soft independent modeling of class analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA) approaches were able to classify the control sample from active mTGase-containing yogurts and enzyme status, but could not differentiate enzyme concentrations. Machine learning effectively determined mTGase presence, activity, and concentrations. In conclusion, NIR and MIR spectroscopy, combined with chemometric methods, successfully detected mTGase in yogurt, with machine learning outperforming SIMCA and PLS-DA in identifying enzyme levels.

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红外光谱结合化学计量学快速检测酸奶中微生物转谷氨酰胺酶的存在、活性和浓度

本研究的目的是建立一种利用近红外（NIR）漫反射和中红外（MIR）光谱快速检测酸奶中微生物谷氨酰胺转胺酶（mtase）的使用、状态（活性或非活性）和浓度的方法。对照样品不加mTGase。采用两种不同的mTGase浓度水平：1和2单位。一半添加酶的样品在酸奶制作后灭活，以检测mTGase的活性/失活状态。通过类类比的软独立建模（SIMCA）和偏最小二乘判别分析（PLS-DA）方法对NIR和MIR进行分析，可以区分对照样品中含有活性mtase的酸奶和酶的状态，但不能区分酶的浓度。机器学习有效地确定了mTGase的存在、活性和浓度。综上所述，近红外光谱和MIR光谱结合化学计量学方法，成功检测了酸奶中的mTGase，机器学习在鉴定酶水平方面优于SIMCA和PLS-DA。

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来源期刊

Innovative Food Science & Emerging Technologies 工程技术-食品科技

CiteScore

12.00

自引率

6.10%

发文量

259

审稿时长

25 days

期刊介绍： Innovative Food Science and Emerging Technologies (IFSET) aims to provide the highest quality original contributions and few, mainly upon invitation, reviews on and highly innovative developments in food science and emerging food process technologies. The significance of the results either for the science community or for industrial R&D groups must be specified. Papers submitted must be of highest scientific quality and only those advancing current scientific knowledge and understanding or with technical relevance will be considered.