No abstract

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

A serious disease that affects the viability of the oil palm industry is basal stem rot, which is caused by Ganoderma. The current level of disease can be viewed as unmanageable, given that the palms were growing in an unfavorable or unsuitable climate. Today, there are numerous approaches to diagnose diseases early, and one of them using molecular methods. Seven genes for early infection markers were effectively generated by a reference's transcriptome study, including LEUCO, ETHYLENE, CHALCONE, ANTHOCYANIDIN, ETHYLENE, MANNOSE, and SENESCENCE. The purpose of this study is to validate and confirm the presence of Ganoderma infections in three endemic oil palm field in Indonesia i.e. Cisalak Baru, Rejosari, and Bekri plantation. This study conducted real time qPCR of RNA from oil palm roots with four different severities of infection. Manual processing of RNA isolation and cDNA synthesis were carried out, to provide quantification expression level. In addition, gene ontology (GO) analysis was also performed in order to explain the roles of each gene tested. The results revealed that CHALCONE is the only marker that consistently elucidate the Ganoderma's early infection appear in three locations. The drawbacks of the analysis results are tightly correlating to the age of oil palm as well as endemic location. GO results declare that seven genes function related to the response of infection. This work was successful in confirming early infection in three places, elucidating the variables influencing the efficacy and sensitivity of molecular detection, and revealing the function and importance of particular genes for detection.