Near-infraredspectroscopy can be used to sensitively track technological steps aimed at soybean germination and other technological operations. The main or rough changes in the seed during these processes can be
near-infraredspectroscopy (NIRS), is able to non-invasively provide information about these changes in oxygenation and hemodynamics in muscle tissue based on the oxygen-dependent characteristics of near-infrared light. NIRS has been utilized to
Ear rots of maize caused by
spp. reduce grain yield and produce mycotoxins, which are harmful to humans and animals. To breed maize cultivars resistant to
spp., reliable large-scale phenotyping is essential. Our objectives were to (i) examine the precision of the ELISA method for determination of important mycotoxins, namely deoxynivalenol (DON) and fumonisins (FUM), (ii) evaluate the potential of near-infrared reflectance spectroscopy (NIRS) to estimate concentrations of DON and FUM in grain produced in inoculated maize plants, and (iii) compare the efficiency of ELISA, NIRS, and visual rating of disease severity for estimation of mycotoxin concentrations. Insignificant variation was observed between duplicate evaluations of DON and FUM by ELISA, showing the high repeatability of this method. DON and FUM determinations by ELISA were more closely correlated with mycotoxin concentrations predicted through NIRS than with visual rating of disease severity. For the prediction of DON, NIRS had very high magnitude of the coefficients of determination of calibration and cross validation (R
= 0.90–0.88). Thus, NIRS has a promising potential to predict DON concentration in grain samples of inoculated maize genotypes evaluated in resistance breeding programs.
Blanco, M., Coello, J., Iturriaga, H., Maspoch, S., Gonzalez, R. (1998) Determination of water in lubricating oils by mid- and near-infraredspectroscopy. Microchim. Acta 128: 235.
Determination of water in lubricating oils by
The use of Fourier-transform near infrared spectroscopy (FT-NIR) to measure the content of protein, lipid and sugar contents of bakery products was investigated. The samples were dried, homogenized, sieved and measured in the wavelength range of 780–2500 nm. The calibration was based on partial least squares (PLS) regression with cross-validation. The performance of the final model was evaluated according to root mean square of cross-validation (RMSECV), root mean square error of estimation (RMSEE) and the determination coefficient (R2).The developed models use the ranges of 1100–1245 nm and 1590–2600 nm for protein determination, 1330–1840 nm and 2170–2350 nm for lipid, 1400–1630 nm, 2000–2170 nm and 2230–2570 nm for sugar determination, respectively. Protein, lipid and sugar could be determined directly with R2 values of 98.93, 99.07 and 98.81, and RMSECV values of 0.16 m/m%, 0.79 m/m% and 0.28 m/m%, respectively. It can be concluded that FT-NIR spectroscopy can be used for the routine determination of protein, lipid and sugar content of bakery products and it can contribute to the estimation of calorie content in a fast and non-destructive way.
Experimental batches of chilled boneless slices of pork meat have been stored aerobically in sterile Petri dishes and total aerobic plate counts (TAPC) and sensorial observations were made periodically during storage to monitor bacterial growth and apparent deteriorative changes at 4, 8 and 12 °C, respectively. Near infrared spectroscopy (diffuse reflectance) measurement was performed on replicate meat samples in the wavelength range of 1000–1800 nm. Second derivative and multiplicative scatter correction were performed on the spectra as data pre-treatments. Principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for observation of discrimination of the samples due to loss of freshness and onset of bacterial spoilage as a function of the storage time. The percentage of correctly classified samples decreased somewhat by increasing the storage temperature. Partial least squares (PLS) chemometric model was developed to predict and quantify bacterial loads from the scatter corrected 2nd derivative spectra. PLS evaluation (predicted versus measured TAPC values) — when bacterial counts at all sampling days and storage temperatures were taken into account — resulted in a correlation coefficient of 0.977, and a root mean square error of prediction (RMSEP) 0.438 log colony forming units/g. These preliminary results indicate the potential of utilising near infrared diffuse reflectance spectroscopy in combination with multivariate statistical methods to monitor loss of freshness and detect bacterial spoilage of meat samples rapidly before deleterious microbial changes become apparent. However, much larger number of samples should be studied to ascertain properly the prediction power of the spectroscopic method.
People have recently started to pay more attention to the healthier lifestyle, which also includes the consumption of more natural and less processed food products. Honey as one of the most often used natural sweeteners has also been reconsidered and more commonly used. However, honey has also been the target of food adulteration due to its emerging use and relatively high price. Therefore, there is an increasing need to develop rapid evaluation methods for the identification of honey from different sources. Experiments have been performed with 79 authentic honey samples of different floral and geographical origins, mainly from Hungary. The standard analytical parameters used to characterize the nutritional values of honey such as antioxidant capacity, polyphenol content, ash content, pH, conductivity have been determined. The samples were also analyzed with a benchtop near infrared (NIR) spectrometer to record their NIR spectra. The data acquired with NIR spectroscopy measurements were evaluated with various univariate and multivariate statistical methods. Results gained with a limited sample set show that NIR spectroscopy might be useful for the identification of floral and geographical origin of honey samples. Further experiments are proposed to build a robust database, which could support the use of NIR spectroscopy as a quick alternative for honey authentication.
Introduction Near-infraredspectroscopy (NIRS) measurements of cerebral and peripheral tissue oxygenation in critically ill neonates for detection of early stages of shock are of increasing clinical interest. Early cardio-circulatory signs of
intramuscular metabolic stress during exercise [ 10–13 ]. Near-infraredspectroscopy (NIRS) allows for non-invasive evaluation of the oxygenation level (a dynamic balance between oxygen supply and utilization) within the localized region of a specific muscle by