Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830–2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954–1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process.

The near-infrared (NIR) spectra of such metals as Cu(II), Mn(II), Zn(II) and Fe(III) in HNO(3) in the 700-1,860 nm region were subjected to a partial least-squares regression analysis and leave-out cross-validation to develop chemometric models. The models yielded a coefficient of determination in cross validation of 0.9744 [Cu(II)], 0.9631 [Mn(II)], 0.9154 [Zn(II)] and 0.741 [Fe(III)]. The regression coefficients for Cu(II), Mn(II) and Zn(II), but not for Fe(III), showed strong negative peaks at around 1,050-1,200 nm, a zone where spectral bands have been reported to decrease with increasing pH value. A positive peak at around 710-750 nm, which may have been due to water absorption, was observed in regression coefficients of Cu(II), Mn(II) and Zn(II) but not in Fe(III), while a negative peak was observed in that for Fe(III) at around 710-750 nm. These results indicate that the divalent cations [Cu(II), Mn(II) and Zn(II)] showed different absorption in the NIR region from the trivalent cation [Fe(III)], suggesting that the vibration mode of water, which mirrors the interaction between cations and water, may be influenced by valency.

ABSTRACT. The risk of infections from zoonotic pathogens of tissues and/or tissue-derived products has been increasing. One preventive approach in reducing infection risk is tissue decontamination, where selection and screening of highly infectious tissues are strictly followed. Therefore, the development of reliable analytical methods for rapid tissue discrimination is essentially important. In the present study, a procedure has been developed for intact tissue discrimination on the basis of multivariate analysis of visible and near-infrared (Vis-NIR) spectra of certain tissues such as brain, liver, kidney and testis of mice without any pretreatment. Transmittance spectra in the 600-to 1000-nm regions were subjected to a principal component analysis (PCA), and leave-out cross-validation was employed to develop multivariate models for tissue discrimination. The plot of PCA scores against Vis-NIR spectra of brains, kidneys, livers and testes from 11 mice portrayed reliable tissue discrimination. This result suggests that Vis-NIR spectroscopy combined with chemometrics analysis may provide a potentially useful approach for rapid non-destructive discrimination of tissues. KEY WORDS: principal component analysis, tissue, Vis-NIR spectroscopy.