Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

Enabling chlorophyll photo-response for in-line real-time noninvasive direct probing of the quality of palm-oil during mill process

During the milling process of palm oil, the degree of palm fruit ripeness is a critical factor that affects the quality and quantity of the oil. As the palm fruit matures, its chlorophyll level decreases, and since chlorophyll in oil has undesirable effects on hydrogenation, bleachability, and oxidative degradation, it's important to monitor the chlorophyll content in palm oil during the milling process. This study investigated the use of light-induced chlorophyll fluorescence (LICF) for non-invasive and real-time monitoring of chlorophyll content in diluted crude palm oil (DCO) located at the dilution and oil classification point in palm oil mill. An LICF probe was installed at the secondary pipe connected to main DCO pipeline, and the system communicates with a computer located in a separate control room via a Wi-Fi connection. Continuous measurements were recorded with an integration time of 500 ms, averaging of 10, and a time interval of 1 min between each recording during the oil mill's operation. All data were stored on the computer and in the cloud. We collected 60 DCO samples and sent them to the laboratory for American Oil Chemists' Society (AOCS) measurement to compare with the LICF signal. The LICF method achieved a correlation coefficient of 0.88 with the AOCS measurements, and it also provided a direct, quantitative, and unbiased assessment of the fruit ripeness in the mill. By incorporating Internet of Things (IoT) sensors and cloud storage, this LICF system enables remote and real-time access to data for chemometrics analysis.

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels-under ripe, ripe, and over ripe-were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets.