Non-Destructive Quantification of Urea Adulteration in Bovine Milk Using Transmittance Multispectral Imaging

2026-08-04

AI Summary

This study addresses the serious threat posed by urea adulteration in milk to food safety by proposing a rapid, non-destructive, and low-cost detection method. For the first time, transmissive multispectral imaging is combined with controlled milk sample density (specific gravity of 1.032) to acquire images across twelve spectral bands ranging from 365 to 940 nm, enabling precise quantification of urea content without reagents or complex pretreatment. Quantitative models based on multiple linear regression (R² = 0.9599) and a feedforward neural network (R² = 0.9773) demonstrate high accuracy under controlled conditions and strong potential for on-site application. This approach offers a novel and practical strategy for screening urea adulteration in dairy products.

Abstract

Adulteration of bovine milk using urea remains a major food quality and health concern, motivating the development of rapid and quantitative screening tools. Conventional approaches, including laboratory-based analytical methods and spectroscopic techniques, have been used for urea detection; however, many remain less suitable for rapid, low-cost routine screening due to requirements such as specialized instrumentation, sample preparation, chemical reagents, or laboratory operation. This study introduces a pragmatic, cost-effective, accurate, and laboratory-validated MSI-based method for quantitative urea estimation under controlled density conditions using a multispectral-imaging-based regression framework. An in-house-built multispectral imaging system operating in twelve discrete spectral bands (365--940~nm) was used to acquire multispectral images of milk samples prepared with controlled urea addition and water for density balancing. Fresh milk was obtained on the day of image acquisition, and the specific gravity of the milk was verified to be 1.032 at 20°C using a hydrometer. Multiple linear regression provided an initial mapping with a high validation $R^2$ of 0.9599, while a feed-forward neural network further improved predictive performance with a validation $R^2$ of 0.9773. These results demonstrate the feasibility of transmittance multispectral imaging for accurate, non-destructive urea quantification under controlled density-balanced conditions, supporting its potential as a rapid screening approach for milk-quality assessment.

Problem

• urea adulteration
• bovine milk
• non-destructive quantification
• multispectral imaging
• food safety

Innovation

• multispectral imaging
• urea adulteration
• non-destructive quantification
• milk quality
• feed-forward neural network