Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms.

Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.

A feasibility study on the use of a pocket-sized NIR spectrometer and multivariate algorithm to distinguish expired drugs from unexpired ones.

An onsite technique for determining drug integrity in sub-Saharan Africa is needed to ensure drug integrity and enhance public health. This current study presents the application of handheld NIR spectroscopic and multivariate techniques for the accurate identification of unexpired drugs from expired ones. A total of 150 drugs comprising 75 drug samples each of antimalarial (40 unexpired and 35 expired) and antibiotics (40 unexpired and 35 expired) were used in the study. Principal component (PC) analysis was used to extract relevant information from the spectral fingerprint and pre-processed using different techniques comparatively to observe the best cluster trends. The performance of three multivariate algorithms: RF, SVM, and PLS-DA were compared after optimization by cross-validation. The results revealed that SVM and PLS-DA were superior with an identification rate for both antimalarial and antibiotic authenticity prediction above 98% at 5 PCs in both the prediction set and calibration set. For simultaneous prediction of expired and unexpired drugs, we achieved a 100% identification rate. Generally, the results show that handheld NIR spectrometers coupled with smartphone devices could successfully be used to identify unexpired antimalarial and antibiotic drugs from expired antimalarial and antibiotic drugs for effective quality assurance in poor-resource countries. This offers positive feasibility for an affordable and user-friendly approach to reducing drug fraud in Africa.