Authors: Universitas Brawijaya | Luluk Oktaviana | Wahyunanto Agung Nugroho | Dimas Firmanda Al Riza
Source: Jurnal Pangan dan Agroindustri (article, 2025-04-30)
Field: Chemistry — Organic Chemistry

Evaluation of Peroxide Value and Free Fatty Acid Content in Coconut Oil and Palm Oil under Different Heating Temperature Treatments using Reflectance–Fluorescence-based computer vision Abstract This study aims to develop a rapid method for assessing the quality of coconut oil and palm oil based on peroxide value and free fatty acid (FFA) content, using a reflectance–fluorescence-based computer vision system. The background of this research lies in the need for a more efficient analytical method compared to conventional laboratory testing, which is often time-consuming and costly. In this study, oil samples were subjected to heating treatments at 180 and 200 °C to simulate frying conditions. The peroxide value and FFA were analyzed using standard laboratory methods, while the computer vision system acquired image data of the oil samples using a reflectance and fluorescence imaging setup. A classification model was developed using a Convolutional Neural Network (CNN) algorithm to automatically extract color features contributing to oil quality classification. The results showed that the *a* (redness) and *b* (yellowness) values increased in samples treated at 180 and 200 °C, indicating Maillard reactions and thermal degradation. Conversely, the *L* value (lightness) decreased due to prolonged exposure to high temperatures. The CNN model was able to classify coconut oil quality with accuracies of 57.07% and 57.41%, while for palm oil, the model achieved accuracies of 85.77% and 94.22%. These findings suggest that the reflectance–fluorescence computer vision approach holds potential as an alternative method for evaluating the quality of cooking oils. Abstrak Penelitian ini bertujuan mengembangkan metode cepat untuk penilaian kualitas minyak kelapa dan minyak sawit berdasarkan nilai bilangan peroksida dan kandungan asam lemak bebas (FFA), berbasis computer vision reflektansi-fluoresensi. Latar belakang penelitian ini didasari oleh kebutuhan akan metode analisis yang lebih efisien dibandingkan pengujian laboratorium konvensional, yang umumnya memerlukan waktu lama dan biaya tinggi. Dalam penelitian ini, sampel minyak diberikan perlakuan pemanasan pada suhu 180 dan 200 °C untuk mensimulasikan kondisi kualitas minyak yang berbeda. Nilai bilangan peroksida dan FFA dianalisis menggunakan metode laboratorium, sementara sistem computer vision memperoleh data dari citra minyak yang diambil menggunakan setup computer vision reflektansi dan fluoresensi. Model klasifikasi dikembangkan dengan menggunakan algoritma Convolutional Neural Network (CNN) yang akan mengekstrak fitur warna yang berkontribusi untuk klasifikasi secara otomatis. Hasil menunjukkan bahwa nilai a (kemerahan) dan b (kekuningan) meningkat pada sampel dengan perlakuan suhu 180 dan 200 °C, yang mengindikasikan reaksi Maillard dan degradasi termal. Sebaliknya, nilai L (kecerahan) menurun akibat lamanya waktu pemanasan pada suhu tinggi. Model CNN mampu mengklasifikasikan kualitas minyak kelapa dengan akurasi masing-masing sebesar 57,07% dan 57,41%, sedangkan akurasi pada minyak sawit mencapai 85,77% dan 94,22%. Temuan ini menunjukkan bahwa pendekatan computer vision reflektansi-fluoresensi berpotensi digunakan sebagai metode alternatif dalam evaluasi kualitas minyak goreng. Peroxide value ; Palm oil ; Coconut oil ; Reflectivity ; Food science ; Fatty acid ; Peroxide ; Palm ; Chemistry ; Acid value ; Pulp and paper industry ; Organic chemistry ; Biochemistry ; Optics ; Engineering Edible Oils Quality and Analysis ; Coconut Research and Applications ; Spectroscopy and Chemometric Analyses