Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94-0.98) and non-invasive measurement through the packaging (NIR; R = 0.95-0.98). The proposed method uses low-cost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain.
Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.
The properties of collagens from Barramundi (Lates calcarifer) skin obtained by acid solubilized (control), pepsin and papain aided extractions were investigated. The yields of collagens (dry weight basis) for acid solubilized, pepsin and papain aided extractions were 8.1, 43.6 and 44.0%, respectively. The collagens were generally colorless although collagens from the enzymes aided-extractions were slightly darker. Based on the e-nose evaluation, the collagens were considered odorless. The pH of all the collagens was in the vicinity of 3; however, those extracted with papain had significantly higher pH. The polypeptide profiles obtained in the SDS-PAGE analysis for pepsin extracted collagen were similar to those of acid solubilized collagens. Papain extracted collagen had distinctly different SDS-PAGE pattern. All the extracted collagens were of type 1 with apparent peptides molecular weight distribution of 37 to 250 kDalton. They had high solubility in pH 2 to 5 and increasing NaCl concentration up to 6%.
In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or "smellprint" emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.
Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.
The influence of temperature-time combinations on non-volatile compound and taste traits of beef semitendinosus muscles tested by the electronic tongue was studied. Single-stage sous-vide at 60 and 70 °C (6 and 12 h), and two-stage sous-vide that sequentially cooked at 45 °C (3 h) and 60 °C (either 3 or 9 h) were compared with traditional cooking at 70 °C (30 min). Umami was better explained in the given model of partial least squares regression than astringency, sourness, saltiness, bitterness, and richness. Sous-vide at 70 °C for 12 h characterized the most umami, likely adenosine-5'-monophosphate (AMP) and guanosine-5'-monophosphate (GMP) as significant contributors. Two-stage sous-vide projected higher histidine, leucine, inosine, and hypoxanthine with the astringent and sour taste significant after 6 and 12 h cooking, respectively. Equivalent umami concentration (EUC) between umami amino acids and umami nucleotides showed a strong relationship to umami taste assessed by the electronic tongue.
This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
In this study, caffeine (CA) was encapsulated into food-grade starch matrices, including swelled starch (SS), porous starch (PS), and V-type starch (VS). The bitterness of the microcapsules and suppression mechanisms were investigated using an electronic tongue, molecular dynamics (MD) simulation and the in vitro release kinetics of CA. All the CA-loaded microcapsules showed a lower bitterness intensity than the control. The MD results proved that the weak interactions between starch and CA resulted in a moderate CA release rate for SS-CA microcapsules. The PS-CA microcapsule presented the longest CA release, up to 40 min, whereas the VS-CA microcapsule completely released CA in 9 min. The CA release rate was found to be related to the microcapsule structure and rehydration properties. A low CA bitterness intensity could be attributed to a delay in the CA release rate and resistance to erosion of the microcapsules. The results of this work are valuable for improving starch-based microcapsules (oral-targeted drug-delivery systems) by suppressing the bitterness of alkaloid compounds.