Assessing macro-element content in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics

International audienceVine fertilization is a tool that allows winegrowers to influence and regulate the quality of their wine. Today, nutritional analysis is done by using a CHNS analyzer and mass spectroscopy. However, these methods are destructive and time consuming. Another approach is to use near-infrared (NIR) spectroscopy, which, when coupled with chemometric tools, allows users to develop prediction models. This approach is widely used today in agriculture. In this study, we focus on the relative amount of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S), in dry matter (DM), and on the C:N ratio. The relative amount of these elements was obtained by applying NIR spectroscopy to 252 samples of various fresh and dried vine organs. Each partial least squares model was tested on an external prediction set. The coefficient of determination for prediction (r(2)), the root-mean-square error of prediction (RMSEP), and the ratio of performance of prediction (RPD) were obtained for C (0.49, 2.24% of DM, and 1.33 for fresh material with MSC; 0.45, 2.37% of DM, and 1.26 for dry material with MSC, respectively), H (0.56, 0.27% of DM, and 1.45 for fresh material with D1; 0.49, 0.30% of DM, and 1.32 for dry material with D1, respectively), N (0.91, 0.17% of DM, and 3.32 for fresh material with raw spectra; 0.95, 0.13% of DM, and 4.39 for dry material with MSC, respectively), S (0.47, 0.046% of DM, and 1.31 for fresh material with MSC; 0.46, 0.046% of DM, and 1.30 for dry material with D2, respectively), and the C:N ratio (0.85, 8.20, and 2.58 for raw spectra of fresh material; 0.87, 7.55, and 2.80 for dry material with D2, respectively). These results show that NIR spectroscopy can be used to assess the status of nitrogen nutrition in vines and to monitor the C:N ratio

Assessing macro- (P, K, Ca, Mg) and micronutrient (Mn, Fe, Cu, Zn, B) concentration in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics

International audienceMacronutrients (phosphorus, potassium, calcium, and magnesium) and micronutrients (manganese, iron, copper, zinc, and boron) play an essential role not only in the general physiology of vines but also in the quality of wine produced. The quantity of each nutrient in the vine is generally determined by analyzing the leaf blades or petioles, but this approach imposes a typical delay of two weeks between sampling and receiving the results, which precludes real-time detection of nutritional deficiencies (e.g., boron deficiency at flowering). Therefore, a method to rapidly analyze vine organs is highly desirable. One candidate for such a method is near-infrared (NIR) reflectance spectroscopy coupled with chemometric methods, based on which winegrowers have already developed prediction models. This approach is widely used today in agriculture. The aim of the present study is to determine whether NIR spectroscopy can be used to obtain accurate information about the nutritional status of vines. In this study, we focus on the mass of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) contained in different vine organs (leaf blades, petioles and berries) over the course of a year. The concentration of these elements was determined based on NIR absorbance spectra from 677 samples of various dried vine organs. Partial least square models for classification and prediction were then developed based on raw and pretreated spectra for each organ, following which the models were tested on an external prediction set. The results show that, for Ca and Mg, all organ models can be used routinely for classification or prediction. For prediction, the Ca (Mg), model produces r2 = 0.88, 0.70, and 0.72 (0.60, 0.72, and 0.80) for leaf blades, petioles, and berries, respectively. Only for leaf blades (berries) is the Ca (Mg) model sufficiently accurate to be used for prediction. For berries, the P, K, and Zn models produce r2 in prediction of 0.77, 0.79, and 0.82, respectively. For petioles, the K model proves reliable for prediction, with r2 = 0.76. The Fe, Cu, and B models produce r2 = 0.72, 0.71, and 0.52, which are suitable for classification but not for prediction. Finally, for leaf blades, the Fe and Cu models produce r2 0.58 and 0.61, respectively, in prediction and thus can be used routinely for classification

Choosing the right delivery systems for functional ingredients in foods: an industrial perspective

The term delivery systems cover a wide range of structuring, encapsulation or formulation technologies for the delivery of a certain functional ingredient in a product to the end user. From addressing colour and taste to physical stability, delivery systems have been pursued by many. Despite enormous research and development efforts, however, delivery systems beyond the standard spray-dried powders and emulsion systems are not widely used in food and beverages. Delivery systems are often difficult to utilise in practice because of incomplete compatibility with the end product, high costs or consumer acceptance. This article provides some industrial insights in selection, design and application of delivery systems in food production