Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers.

Spectra data from two instruments (UV-Vis/NIR and FT-NIR) consisting of three and one detectors, respectively, were employed in order to discriminate the geographical origin of milk as a way to detect adulteration. Initially, principal component analysis (PCA) was used to see if clusters of milk from different origins are formed. Separation between samples of different origins were not observed with PCA, hence, feed-forward multi-layer perceptron artificial neural network (MLP-ANN) models were designed. ANN models were developed by changing the number of input variables and the best models were chosen based on high values of generalized R-square and entropy R-square, as well as small values of root mean square error (RMSE), mean absolute deviation (Mean Abs. Dev), and -loglikelihood while considering 100% classification rate. Based on the results, whether the spectra data was collected from a single or three detector instrument the same clustering was observed based on geographical origin.

Classification and quantification of palm oil adulteration via portable NIR spectroscopy.

Short wave near infrared spectroscopy (NIR) method was used to detect the presence of lard adulteration in palm oil. MicroNIR was set up in two different scan modes to study the effect of path length to the performance of spectral measurement. Pure and adulterated palm oil sample were classified using soft independent modeling class analogy (SIMCA) algorithm with model accuracy more than 0.95 reported for both transflectance and transmission modes. Additionally, by employing partial least square (PLS) regression, the coefficient of determination (R2) of transflectance and transmission were 0.9987 and 0.9994 with root mean square error of calibration (RMSEC) of 0.5931 and 0.6703 respectively. In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. The result of R2 and RMSEC after variable selection for transflectance and transmission were improved significantly. Based on the result of classification and quantification analysis, the transmission mode has yield better prediction model compared to the transflectance mode to distinguish the pure and adulterated palm oil.