Scholarlink

<p>Ghee is highly valued for its rich flavour and nutritional benefits, making it a vital part of the human diet. However, its premium price often results in adulteration with cheaper fats. This study utilized Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometrics to detect the addition of buffalo body fat (BBF) to ghee. Pure mixed ghee (PMG), BBF and BBF-adulterated PMG were analysed in the wavenumber range of 4000 –500&#xa0;cm⁻¹. Chemometric techniques, including Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were employed on the FTIR data for the detection of BBF. PCA demonstrated distinct clustering patterns in the regions of 2940 –2820&#xa0;cm⁻¹ and 1770 –1720&#xa0;cm⁻¹, successfully distinguishing pure ghee from adulterated samples. SIMCA classification achieved 100% accuracy in identifying pure ghee samples, demonstrating its robust classification efficiency. The study confirmed that ATR-FTIR and chemometrics could detect BBF adulteration at levels as low as 1%, highlighting its potential as a rapid and reliable method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Detection of buffalo body fat in ghee using ATR-FTIR spectroscopy coupled with chemometrics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

查看全文
  • Onkar Gaikwad,
  • Kamal Gandhi,
  • Sonu K. Shivanna,
  • Rajan Sharma,
  • Rajesh Kumar Bajaj

摘要

Ghee is highly valued for its rich flavour and nutritional benefits, making it a vital part of the human diet. However, its premium price often results in adulteration with cheaper fats. This study utilized Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometrics to detect the addition of buffalo body fat (BBF) to ghee. Pure mixed ghee (PMG), BBF and BBF-adulterated PMG were analysed in the wavenumber range of 4000 –500 cm⁻¹. Chemometric techniques, including Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogy (SIMCA) were employed on the FTIR data for the detection of BBF. PCA demonstrated distinct clustering patterns in the regions of 2940 –2820 cm⁻¹ and 1770 –1720 cm⁻¹, successfully distinguishing pure ghee from adulterated samples. SIMCA classification achieved 100% accuracy in identifying pure ghee samples, demonstrating its robust classification efficiency. The study confirmed that ATR-FTIR and chemometrics could detect BBF adulteration at levels as low as 1%, highlighting its potential as a rapid and reliable method.