NIR Spectroscopy for Rapid Freshness Assessment and Quality Classification of Chicken Eggs

Priti Prakash Patil; Vijay N. Patil

doi:10.35516/jjas.v21i1.2121

Authors

Priti Prakash Patil Savitribai Phule Pune University Pune, India. https://orcid.org/0000-0002-3532-5855
Vijay N. Patil Savitribai Phule Pune University Pune,India https://orcid.org/0000-0002-1217-2112

DOI:

https://doi.org/10.35516/jjas.v21i1.2121

Keywords:

Classification of eggs, , Freshness of chicken eggs, NIR Spectroscopy, Non-destructive method, , MSC, SNV, SG 1st Derivate, PLS-R, SVM-R, PLS-DA, SVM-C

Abstract

Eggs undergo significant alterations during storage, which results in a loss of quality. To keep an eye on the freshness and quality of the eggs, it is essential to predict these changes. This study's objective was to assess the use of visible Near infrared (NIR) spectroscopy, which is a quick, non-destructive, online method for evaluating the quality of eggs. During the study six hundred sixty whole fresh eggs with white shells produced by the same group of hens fed a typical feed was acquired. They were placed in temperature-controlled environments whose temperature was 20°C and 30°C respectively and observed their spectra for 25 days of storage. The spectra of 40 eggs were collected for 0, 4,7,10,14,17,19,21,25 days within the NIR spectral range of 902 to 1810 nm; the absorption spectrum data was found to be collected for every 4nm span. The spectral non-destructive data was contrasted with the Haugh Units (HU) of the egg sample in terms of freshness and to the quantity of storage days in terms of quality. This study explores the potential of Near-Infrared (NIR) spectroscopy combined with chemometric analysis for non-destructive egg quality assessment, focusing on predicting Haugh Units (HU) and storage duration. The research involved systematic data collection, preprocessing of NIR spectra, and developing predictive models using Partial Least Squares (PLS) regression. Results demonstrated a high accuracy in predicting HU values and storage duration, with an R² value of 0.986 for calibration. Eggs stored at 20°C maintained higher HU values than those stored at 30°C, significantly impacting freshness assessment. Principal Component Analysis (PCA) effectively reduced data dimensionality, enhancing model precision. Combining shell measurement areas and preprocessing techniques improved PLS-DA model performance, achieving up to 95.75% accuracy in classifying egg freshness. The findings highlight the practical application of NIR spectroscopy and chemometric analysis in the food industry for ensuring egg quality and freshness.

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Author Biographies

Priti Prakash Patil, Savitribai Phule Pune University Pune, India.

Research Scholar, SVPM's College of Engineering Malegaon BK Baramati. Savitribai Phule Pune University Pune, India.

Vijay N. Patil, Savitribai Phule Pune University Pune,India

Research Guide, SVPM's College of Engineering Malegaon BK Baramati. Savitribai Phule Pune University Pune, India.

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