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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Ghazali, Rashidah
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Computer Science & IT Engineering

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Broiler meats tenderness prediction using near infrared spectroscopy against non-linear predictive modelling Ghazali, Rashidah; Abdul Rahim, Herlina; Nurani Zulkifli, Syahidah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2713-2723

Abstract

Near infrared (NIR) spectroscopy is a non-invasive analytical technique known for its ability to assess the quality attributes of meat products. However, the linear models utilized, partial least square (PLS) and principal component regression (PCR) achieved unsatisfactory performances of meat physical attributes prediction. Hence, in this research, for its inherent advantages in modelling nonlinear system, artificial neural network (ANN) is augmented to the components of PCR and PLS. Through the augmentation, the principal component neural network (PCNN) and latent variable neural network (LVNN) models are developed. From the results obtained, it shows that PCNN and LVNN successfully surpassed their respective linear versions of PCR and PLS by 70% higher shear force prediction performances. The LVNN proved to achieve the best prediction in breast meat with root mean square error of prediction (RMSEP) of 0.0769 kg and coefficient of determination (RP2) of 0.8201 whilst for drumsticks, RMSEP=0.1494 kg and RP2=0.8606. NIR spectroscopy technology integrated with machine learning yields a promising non-invasive technique in predicting the shear force of intact raw broiler meat.
Prediction of broiler shear force using near infrared spectroscopy with second derivative linear modeling Ghazali, Rashidah; Rahim, Herlina Abdul; Zulkifli, Syahidah Nurani
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1787-1794

Abstract

This study explores the use of linear predictive models, specifically principal component regression (PCR) and partial least squares (PLS), in combination with a cost-effective near infrared spectroscopy (NIRS) system to noninvasively assess the texture of raw broiler meat. The findings demonstrate that appropriate pre-processing techniques, such as excluding the visible spectrum and applying the second-order Savitzky-Golay (SG) derivative with an optimal filter length (FL), enhance model performance. Notably, the PLS model outperformed PCR, requiring fewer latent variables (LVs) to achieve accurate predictions. This suggests that PLS more effectively captures key spectral features associated with meat texture, making it a promising approach for assessing raw broiler meat quality in a practical, cost-efficient, and non-invasive manner. These results highlight the potential of integrating linear predictive models with NIRS technology for reliable texture analysis in the poultry industry.
Co-Authors Herlina Abdul Rahim, Herlina Nurani Zulkifli, Syahidah Rahim, Herlina Abdul Zulkifli, Syahidah Nurani