Nutritive evaluation of olive tree leaves by near-infrared spectroscopy: effect of soil contamination and correction with spectral pretreatments

Near infrared spectroscopy for the analysis of macro and micro nutrients in sugarcane leaves

About ten years ago, NIR technology was implemented at the Colombian Sugarcane Research Center Cenicaña for the analysis of sugarcane quality, followed by the development of methodologies for the quantification of some elements in soils and sugarcane leaves. Methodologies were based on the presence of high content materials, such nitrogen, potassium, calcium, and magnesium; however, determination of micronutrients which are found in soils and leaves in the range of parts per million has been challenging. Development of a NIR methodology for quantification of macro (N, P, K, Ca, Mg) and micronutrients (Cu, Zn, Mn, Fe) in a single experiment is here reported. Calibration curves were constructed using approximately 500 samples that were previously analyzed by methods of reference such atomic absorption. Statistical analysis of the data showed that there are not significant differences between the methods of reference and NIR, suggesting that NIR is a very fast, economical and convenient methodology for the daily analysis of sugarcane leaves.

Direct prediction of bioethanol yield in sugar beet pulp using near infrared spectroscopy

Sugar beets are a raw material for the production of sugar and ethanol. The decision on which end product to pursue could be facilitated by fast and reliable means of predicting the potential ethanol yield from the beets. A Near Infrared (NIR) Spectroscopy-based approach was tested for the direct prediction of the potential bioethanol production from sugar beets. A modified partial least squares (MPLS) regression model was applied to 125 samples, ranging from 21.9 to 31.0 gL(-1) of bioethanol in sugar beet brei. The samples were analyzed in reflectance mode in a Direct Contact Food Analyser (DCFA) FOSS-NIRSystems 6500 monochromator, with standard error of cross validation (SECV), standard error of prediction (SEP), coefficient of determination (r(2)) and coefficient of variation (CV) of 0.51, 0.49, 0.91 and 1.9 gL(-1), respectively. The NIR technique allowed direct prediction of the ethanol yield from sugar beet brei (i.e. the product obtained after sawing beets with a proper machine) in less than 3 min.

Determination of total ash and acid-insoluble ash of Chinese herbal medicine Prunellae Spica by near infrared spectroscopy

Rapid and accurate determination of total ash and acid-insoluble ash of Chinese herbal medicine would be valuable for the quality illustration because current approaches are laborious and time-consuming. This study investigated the feasibility of near infrared spectroscopy in predicting simultaneously the total ash and acid-insoluble ash contents of Prunellae Spica (one of the most widely used Chinese herbal medicine). Samples were collected from ten different geographic regions and scanned under the near-infrared spectroscopy region (4000-12000 cm(-1)). Models between the spectral data and the results of reference analysis were developed, and subsequently validated. Calibration models based on partial least squares were accurate for the prediction of total ash (R2=0.914; standard errors of prediction=0.373), and acid-insoluble ash (R2=0.905; standard errors of prediction=0.452).