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

Highlights

• NIR spectroscopy and chemometric were used to develop nutritional diagnosis of vine.

• NIR spectra were acquired from dried samples of leaf-blades, petioles, and berries.

• NIR spectroscopy can determine the status of macro/micronutrients in vine organs.

Abstract

Macronutrients (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.

Introduction

Macronutrients (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, and the specific role played by each of these nutrients is quite well known and documented (Carbonneau, 1984, Crespy, 2010). For example, the role of the macronutrient potassium is extensively documented, both regarding physiology and wine quality. At the vine level, potassium helps to regulate the main physiological mechanisms, notably the transpiration mechanism, opening and closing of the stomata. In wine, however, excess potassium is frequent and leads to diminished acidity and reduced conservation time (i.e., faster oxidation and unstable coloring) (Crespy, 2010, Galet, 2000, Hopkins, 2013, Reynier, 2011, Rogiers et al., 2017). For micronutrients, consider the example of boron. Although the precise form of boron found in vines is not well elucidated or documented, studies based on induced boron deficiency indicate that boron-deficient plants not only suffer from anomalous cell structure (especially the walls) but also excessive flowering coulure symptoms (symptomatic of low rates of pollination). For wine, boron deficiency or excess leads to an unpleasant acidity (Crespy, 2010, Hopkins, 2013).

Fertilization management is therefore a vital strategy for winegrowers, and recommendations are most often based on a foliar or petiolar diagnoses (Bessis, 1967, Carbonneau, 1984). These techniques involve acquiring samples, sending the samples to an external laboratory, and then waiting for the results for interpretation. A delay of two weeks is common between sampling and receiving the results, which precludes real-time detection of nutritional deficiencies (e.g., boron deficiency at flowering). These issues are crucial in France because the wine sector produces the second-highest external trade balance of all economic sectors (9.36 billion euros in 2018) (FranceAgriMer, 2019). Therefore, a method to rapidly diagnose vine organs is highly desirable.

Near-infrared (NIR) spectroscopy coupled with chemometric tools is a possible approach to satisfy this demand for rapid analysis of vine physiology. The samples for NIR spectroscopy require little or no preparation; drying and grinding often suffice to limit the effects of water on the spectral absorbance. After acquiring NIR absorbance spectra, chemometric techniques are used to develop models that, if sufficiently accurate, may be used routinely to determine the various physico-chemical parameters (e.g., quantity of starch) (Agelet and Hurburgh, 2010, Tuffery, 2011).

NIR absorbance spectra reveal the C–H, O–H, and N–H vibrational modes and thereby provide information on the organic matrix. Since macro- and microminerals do not have these vibrational modes, they cannot be directly detected in NIR spectra. However, since these minerals are present in the organic matrix and combine with one of the organic functional groups mentioned above, their concentration can be indirectly determined (Cozzolino et al., 2008, Ward et al., 2006).

NIR technology has been widely applied in the food industry to determine component concentrations in foodstuffs, such as the amount of starch in rice, water in cheese, or alcohol in wine (Cen and He, 2007). Several studies have focused on vines and wine, with spectra acquired in the field or in the laboratory; however, these studies aimed on the parameters that determine the quality of grape berries for vinification, such as pH, total soluble sugar, or polyphenol concentration (Dambergs et al., 2015). Considering more specifically the composition of certain vine organs, an Australian study investigated the sugar and starch concentration and the sum of the two (i.e., total nonstructural carbohydrates) in ground and dried Chardonnay trunks and leaves (De Bei et al., 2017). In previous work, we studied the use of NIR spectroscopy models to predict the concentration of elementary bricks (i.e., carbon, nitrogen, hydrogen, and sulfur) in various vine organs, both fresh and dried. NIR reflectance spectra were pretreated by applying multiplicative scatter correction (MSC) for carbon and sulfur and the Savitzky–Golay first derivative (D1) method for hydrogen (Cuq et al., 2020). In other work, the macro- and micronutrients and electroconductivity of grape berry juice were investigated by studying fresh crushed berries of Cabernet Sauvignon, Shiraz, Merlot, Pinot Noir, and Chardonnay grapes. The spectra were again pretreated by applying a MSC. The model produced an r² for the prediction set ranging from 0.30 for iron to 0.77 for the electroconductivity of the juice. However, applying the model to the calibration set for sulfur, magnesium, and electroconductivity produced good ratios of performance deviation (RPDs) of 2.4, 2.2, and 2.0, respectively, suggesting that the prediction results improve upon increasing the number of measurements (Cozzolino et al., 2011).

The present work aims to develop a NIR spectroscopy tool to enable winegrowers and technical advisers to rapidly determine the physiology of their vines (where “rapidly” means within two to three days). The study focuses on using NIR spectroscopy to assess the mineral concentration in different vine organs so that winegrowers can respond accordingly if a deficiency or excess is detected. The minerals in question are phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) in dried and crushed vine leaf blades petioles, and berries.