Art_15479.indd Journal of Applied Botany and Food Quality 94, 26 - 38 (2021), DOI:10.5073/JABFQ.2021.094.004 1 State Research Institute for Viticulture and Pomiculture, Weinsberg, Germany 2 University of Hohenheim, Institute of Crop Science, Quality of Plant Products (340e), Stuttgart, Germany Near-infrared spectroscopy in process control and quality management of fruits and wine Jana Gehlken1,2, Martin Pour Nikfardjam1, Melissa Kleb2, Christian Zörb2* (Submitted: September 27, 2020; Accepted: January 27, 2021) * Corresponding author Summary Recently, rapid quality assessment of food is an increasingly impor- tant topic. The rising demand of consumers for high quality products generates a need to establish fast and suitable analytical methods. Near-infrared (NIR) spectroscopy has turned out to be a time-saving, cheap, easy-to-use and environmentally friendly technique, which can be applied for the determination of manifold quality attributes in various kinds of food matrices. This article gives overview of the basic principles of near-infrared measurements and describes the im- mense field of applications, with the main focus on fruits, grapes and wine and the evaluation of wine aroma. Keywords: near-infrared spectroscopy, NIR, process control, quali- ty management, fruit analysis, grape and wine analysis, wine aroma Introduction The safety and accurate quality assessment of food is becoming increasingly important in the last decades. Various affairs in food safety and quality (e.g. BSE, horse meat in ready meals, Fipronil in eggs) (EuropEan Food SaFEty authority; World hEalth organization) trigger development and continuous improvement of control systems for food safety and food quality with consumer protection as a main goal. A set of methods has been evolved in the last decades including improved classical wet-lab methods or quick methods. Most classical wet-lab methods give precise and accurate results, but assets and drawbacks may come along. High-performance liquid chromatography (HPLC) or gas chromatography (GC), often coupled with mass spectrometry (MS), require experienced users, expensive technical equipment and ultrapure, high priced chemicals, which additionally hold the risk of harming the user’s health and/or may affect the environment if not used adequately. Another main short- coming is the time requirement for those methods, which may take several hours or even days to produce results. NIR spectroscopy increasingly gains attention, because it is easy to use, cheap, provides results quickly and direct implementation in production processes is possible. Thus, the delay of time caused by sampling, analysis and evaluation can be avoided, so immediate intervention in case of irregularities is possible, which reduces the waste of resources and economic losses. Furthermore, the consum- er’s awareness for quality characteristics of food (presence/absence of various ingredients, origin of the product, cultivation technique or manufacturing process) is growing. The use of NIR spectroscopy enables continuous monitoring of these attributes to meet consumer’s expectations and prevent food fraud. Development of instrumentation and industrial applications of NIR spectroscopy began to increase in the second half of the 20th cen- tury. First applications to agricultural products were done in 1964 by Norris who recognized the potential of this technique already at the early stages of development (SiESlEr, 2002). Since then, the field of application has expanded (> 700,000 results for NIR analysis of food, Google Scholar, July 2020) just as the diversity of instrumentation, for instance on-line measurements and portable devices (alEixandrE- tudo et al., 2019; amuah et al., 2019; FErnándEz-novalES et al., 2019; Beć et al., 2020). The objective of this article is (i) to review the basic principles of near-infrared measurements and (ii) to give a detailed overview of applications with the main focus on fruit, grape and wine quality control and the option to evaluate wine aroma by the use of NIR spectroscopy. Basic principles of near-infrared measurements Near-infrared radiation was discovered by Friedrich Wilhelm Herschel in the year 1800 (hErSchEl, 1800) and covers the range between mid-infrared and visible light in the electromagnetic spec- trum (780 to 2500 nm). When near-infrared radiation hits a sample, the radiation is absorbed, transmitted or reflected, which in each of the three cases depends on the chemical composition and the physi- cal properties of the sample (nicolaï et al., 2007). The near-infrared spectrum is dominated by overtone and combina- tion bands of CH, OH and NH functionalities and, in case of moist samples, water, which shows two very strong and broad absorp- tion bands in the near-infrared region (SiESlEr, 2002; BurnS and ciurczak, 2008; Beć and huck, 2019). The absorption bands often overlap, which results in very low structural selectivity and impedes the interpretation of spectra and especially qualitative evaluation (næS et al., 2004). For this reason, it is necessary to use multivariate statistical techniques (often called chemometrics) to extract the in- formation linked directly to the desired compounds for the develop- ment of calibration models. Additionally, irrelevant information has to be suppressed in the spectra, which can be achieved by various preprocessing techiques (nicolaï et al., 2007). Even within the same matrix, the use of different preprocessing techniques can result in varying effects on the resulting calibration models (purWanto et al., 2015; khodaBakhShian et al., 2016; xu et al., 2017) just as the use of different calibration techniques (giovEnzana et al., 2015; altiEri et al., 2017; malEgori et al., 2017). The validation of calibration models is essential to assure their prediction accuracy and can either be performed internally (cross-validation) or with an independent set of samples. Statistic values, which are often used to describe the ac- curacy of a model, are the root mean square error (RMSE), the corre- lation coefficient (r) or the multiple correlation coefficient (R2) (næS et al., 2004). Applications of near-infrared spectroscopy Applications in process control and quality management of fruit and wine industry For the successful application of NIR spectroscopy, a number of con- ditions has to be fulfilled. First, the compound or property has to have an influence on the NIR spectrum. In case of such an influence NIR spectroscopy in process and quality control 27 mostly a relationship exists between intensity of absorption bands and concentrations. Relationships can also be indirect, when a com- pound or property is correlated with another one, which is directly related to the spectrum. The samples for building the model must be selected according to the concentration range of the examined com- pound and must cover all sources of variability, e.g. different seasons or cultivars. Furthermore, the sources of error have to be identified to evaluate and to improve the precision of the model. Finally, the validity over time needs to be given by regular maintenance (macho and larrEchi, 2002). Qualitative analysis is based on grouping samples according to selected properties and is mainly used for identification purposes and quality judgement. Classification by NIR spectroscopy can be more precise than by mid-infrared (MIR) spec- troscopy, but appropriate mathematical operations are needed for every model to minimize misclassifications. For quantitative analy- sis, Lambert-Beer’s law is applied, which describes a linear relation- ship between concentration and absorbance at a given wavelength (SiESlEr, 2002). Quantitative analysis often shows very good and reliable performance for compounds present in high amounts, but often decreases for lower concentrations (nicolaï et al., 2007). In general, the detection limit is about 0.1% (m/m), however, lower values can be reached in some cases, depending on the application and the characteristics of sample and analyte matrix (paSquini, 2003). The main areas of application of NIR spectroscopy are petrochem- istry, environmental analysis, the clinical and pharmaceutical sector and the agricultural food sector (Blanco and villarroya, 2002). The food sector represents a very extensive field of raw materials, intermediates and products. Therefore, the establishment of a uni- form procedure for quality assessment would be of great interest for producers and consumers. For instance, grain and grain products belong to the earliest applications of NIR spectroscopy for quality control in the food sector (WilliamS et al., 1985). Most applications to grain and grain products such as flour focus on the determination of compounds like protein content, moisture, ash (manlEy et al., 2013) or dietary fiber content (kayS et al., 1996; archiBald and kayS, 2000; kayS and Barton, 2002; zörB et al., 2018), which all represent important quality parameters. Nowadays, for the determi- nation of the protein content, a portable NIR spectrometer can be used (Bhandari et al., 2004). Many but not all quality characteris- tics of bread are correlated with the protein content (zörB et al., 2018). Moreover, the suitability of NIR spectroscopy as a determina- tion method for contaminants in grains was shown for ergot bodies in cereals (vErmEulEn et al., 2013). Other application examples for meat, milk and their derived pro- ducts as well as for vegetables are described elsewhere (huang et al., 2008; qu et al., 2015). Applications for fruit analysis The NIR spectroscopy is a suitable method for the determination of fruit maturity, scheduling of harvesting time, quality assessment of fruit products or the detection of possible contaminations. Tab. 1 gives an overview of current applications of NIR spectroscopy in fruit analysis. The application of NIR spectroscopy may start at the field for moni- toring plant nutrient status, for instance the leaf nitrogen status in pear orchards (Wang et al., 2017). It was also possible to monitor the infestation of two different species of larvae, which can affect apples by NIR spectroscopy (SiEgWart et al., 2015). In affected fruits, two larva species could be differentiated. The method can help to reduce pesticides and might serve for the development of alternative crop protection methods. Even fruits grown at the same site, under similar conditions and harvested similarly can vary in size, color, shape and biochemical composition, which may result in a different inner and outer quality. To avoid harvesting of fruits, which have not reached criteria for maturity, adequate parameter determination of the best time for harvesting can be achieved quickly and simultaneously by NIR spectroscopy. Some of those measurements in the field were available using special portable devices, e.g. for blueberries (riBEra- FonSEca et al., 2016b). To protect the post-harvest quality of fruits within a possible storage or transport time, it is important to know the optimum conditions for storage and for transport (kiEnzlE et al., 2011). Post-harvest quality has to be controlled constantly depending on fruit characteristics, for example when fruits are climacteric, or in case of differing mar- keting destination, such as direct or later sale, food processing or export. Besides visible changes like shape or color, changes in bio- chemical composition can occur, which may also affect consumer’s acceptance. Fruits for immediate sale must be free of damages and spoilage. In various studies (lorEntE et al., 2015; Folch-Fortuny et al., 2016; WEdding et al., 2019), damages and infections were iden- tified by NIR spectroscopy even if barely visible. For example, based on NIR measurements, peaches were classified due to different sus- ceptibility to brown rot (Spadoni et al., 2016). Moreover, the number of days before the decay of peaches could be determined (huang et al., 2017). Therefore, recommendations for the best time to eat the fruit can be provided based on the results of NIR measurements. Furthermore, the results can help to optimize storage conditions and to decide on marketing destinations to avoid economic losses and minimize food waste, e.g. in case of damages or short shelf-life. Besides outside appearance, taste plays a key role in the consumer’s acceptance. The contents of sugar and acid often determine whether the taste is conceived as pleasant. In addition, the sugar/acid ratio and the BrimA (Brix minus acids) value (Jordan et al., 2001) can serve as flavor indices (ncama et al., 2017). Fruits with high contents of dry matter are often preferred by the consumers, which has been observed in sensory evaluation of mangos (doS SantoS nEto et al., 2018) and pears (SErra et al., 2019) classified by their dry matter content using NIR spectroscopy. Due to the increasing interest in foods with health benefits, the consumers may pay more attention to the content of various compounds, which are claimed to have posi- tive health effects. Therefore, it is favorable to implement rapid and precise methods for non-destructive analysis of those valuable sub- stances. Despite the low concentration of several of these interesting compounds, good or excellent results could be achieved by the use of NIR spectroscopy to monitor the quality of fruits and fruit products. Climate change aspects and environmental pollution may further in- tensify the consumer’s awareness for food derived from sustainable cultivation or of regional origin. Simultaneously, this trend may en- hance the risk of food fraud further making it necessary to establish a fast and easy method for supervision. In several studies, e.g. origin discrimination of Chinese quince fruits (Shao et al., 2017), discrimi- nation between different farming systems for strawberries (amodio et al., 2017) and pineapples (amuah et al., 2019) and discrimination between naturally and artificially ripened mangos (lakadE et al., 2019) was possible with high accuracies by the use of NIR spectros- copy. Despite these examples, it might further be difficult to deter- mine the origin of production of diverse plant products and one has to be further critical with these reports. As shown in Tab. 1, NIR spectroscopy is widely used in fruit and fruit product analysis and numerous studies on manifold quality attributes are available. However, in many cases the calibration needs to be further validated by larger sets of samples to stabilize the calibra- tion and improve prediction accuracy. Additionally, a larger variety of properties should be examined to check, in which cases global or separated calibration models are needed for analysis. Specific spec- troscopy-based devices, such as the DA-meter, show the potential of NIR technique for the food sector. The DA-meter is a portable spec- trometer, which measures the index of absorbance difference (IAD). 28 J. Gehlken, M. Pour Nikfardjam, M. Kleb, C. Zörb F ru it C om po un ds F ru it M at ur it y F ru it Q ua lit y Tab. 1: Applications of NIR spectroscopy in fruit analysis Fruit Parameters/Properties R2 Reference Acerolaa Titratable acid 0.66 – 0.74 malEgori et al., 2017 Ascorbic acid 0.40 – 0.65 Avocado Moisture content 0.91 – 0.95 olarEWaJu et al., 2016 Dry matter 0.91 – 0.95 Mesocarp oil content 0.67 – 0.71 Mangoa Total soluble solids 0.92, 0.87 naScimEnto marquES et al., 2016; Dry matter 0.67, 0.84 doS SantoS nEto et al., 2017 Titratable acid 0.50, - Firmness 0.72, - Peacha Flesh firmness 0.80 uWadaira et al., 2018 Water soluble pectin 0.82 Pomegranate Total soluble solids 0.93 (r) khodaBakhShian et al., 2017b Titratable acid 0.94 (r) pH value 0.84 (r) Avocado Bruises 60 – 100b WEdding et al., 2019 Rot 65 – 84b Citrus fruits Decay lesions 90 – 96b Folch-Fortuny et al., 2016 Fungal infections 98b lorEntE et al., 2015 Date Fruit Total soluble solids 0.97 alhamdan and atia, 2017 Moisture content 0.94 Colour (b*) 0.64 Kiwifruit Total soluble solids 0.58 – 0.83 li et al., 2017 Flesh firmness 0.30 – 0.60 Mangoa Dry matter 0.75 doS SantoS nEto et al., 2018 Internal browning 83.1b gaBriëlS et al., 2020 Peach Days before decay 82.26b huang et al., 2017 Pear Maturity stage 97.2b xu et al., 2017 Persimmon Soluble tannins 0.92 cortéS et al., 2017 Strawberry Storage time 97.4b ShEn et al., 2018 Açai Anthocyanins 0.74 dE almEida tEixEira et al., 2015 Apple (peel/flesh) Phenolic compounds 0.91/0.84 piSSard et al., 2018 Berry fruits Phenolic compounds 0.98 Gajdoš Kljusurić et al., 2016 Antioxidant activity 0.99 Blackberry Carotenoids 0.71 tolEdo-martín et al., 2018 Phenolic compounds 0.65 Castanhola Anthocyanins 0.80 câmara coSta et al., 2017 Phenolic compounds 0.82 Juçara Anthocyanins 0.73 dE almEida tEixEira et al., 2015 Jujube Total sugar 0.90 guo et al., 2016 Total acid 0.98 Phenolic compounds 0.94 Antioxidant activity 0.96 Mango Soluble solids content 0.68 purWanto et al., 2015 Titratable acid 0.69 Persimmon Colour index 0.99 altiEri et al., 2017 Firmness 0.99 Soluble solids content 0.98 Titratable acid 0.98 Soluble tannins 0.99 Pineapplea Total soluble solids 0.85 amuah et al., 2019 Pomegranate Firmness 0.94; - khodaBakhShian et al., 2016; Total soluble solids 0.94; 0.97 khodaBakhShian et al., 2017a Titratable acid -; 0.93 pH value 0.88; 0.94 Strawberry Soluble solids content 0.83 mancini et al., 2020 Firmness 0.54 NIR spectroscopy in process and quality control 29 Watermelon Total soluble solids 0.71 tamBurini et al., 2017 Lycopene 0.81 β-Carotene 0.74 Wax jambu Anthocyanins 0.98 viEgaS et al., 2016 Phenolic compounds 0.94 Apple juice Z. rouxii contamination 0.99 niu et al., 2018 Cashew apple nectar Total sugar 0.71 caramêS et al., 2017 Total soluble solids 0.62 Titratable acid 0.75 pH value 0.75 Ascorbic acid 0.84 Guava nectar Total sugar 0.82 caramêS et al., 2017 Total soluble solids 0.88 Titratable acid 0.79 pH value 0.76 Ascorbic acid 0.86 Guava pulp Total sugar 0.72 alamar et al., 2016 Total soluble solids 0.66 Titratable acid 0.79 Moisture 0.94 Ascorbic acid 0.85 Kiwi juice Z. rouxii contamination 0.99 niu et al., 2018 Strawberry juice Soluble solids content 0.98 WlodarSka et al., 2019 Phenolic compounds 0.84 Strawberry powder Freshness of fruits 97b Wang et al., 2019 Various fruit juices Saccharin adulteration 98b maBood et al., 2018 Yellow passion fruit pulp Total sugar 0.90 alamar et al., 2016 Total soluble solids 0.54 Titratable acid 0.59 pH value 0.61 Moisture 0.93 a Portable NIR spectrometer used b Classification rate [%] F ru it P ro du ct Q ua lit y The IAD describes the difference in chlorophyll absorbance at 670 nm and 720 nm and can be used to pre-estimate fruit ripeness (zioSi et al., 2008). Applications for grape and wine analysis As well as for other fruits, NIR spectroscopy may be used for moni- toring grape quality and the following steps of winemaking. Tab. 2 shows various applications in viticulture, grape quality and wine analysis. In vineyards, water availability is an important aspect for grapevine growth. Due to climate change, irrigation becomes increasingly im- portant in viticulture to avoid negative effects of water stress for plant vigor and the resulting wine quality. To measure plant water status, NIR spectroscopy can be used directly in the vineyard (giovEnzana et al., 2018). NIR spectroscopy was used as an on-the-go spectral measurement to obtain information about the leaf water status of vines (diago et al., 2018). The spectrometer with an operation wave- length from 1100 - 2100 nm was mounted on an all-terrain vehicle with a motion speed of 5 km/h in the vineyard. To prove the data set from the NIR spectrometer, the stem water potential was used for correlation purpose. This study showed two absorption peaks at about 1450 nm, which corresponded to the first overtone of the symmetric and asymmetric hydroxyl (OH) bond stretching and/or combination bands and around 1940 nm, which can be assigned to the combination of the OH stretching and bending bands of water (diago et al., 2018). Those stretching, bending and combinations are vibrational reactions of the organic groups provoked by the electro- magnetic excitation of the NIR spectroscopy. Due to the fact that leaves have high water content, the prevalence of the OH group absorbance and the corresponding NIR spectra is proven (nicolaï et al., 2007). Other literature shows water-related bands at 975, 1200, 1470, 1930 and 2500 nm (clEvErS et al., 2010; cao et al., 2013). The results may contribute to the development of better irrigation scheduling to reach higher irrigation efficiency. In addition to water, the grapevine requires a number of mineral elements to grow, flower and to produce fruit (WhitE, 2015). For instance, the concentration of nitrogen, sulfur and the C/N-ratio in vine leaves and grape berries was determined by NIR spectroscopy (cuq et al., 2020). However, calibration models with high accuracy were reported for nitrogen (R2 = 0.95) and for the C/N-ratio (R2 = 0.87) in leaves and berries. Based on these results, sustainable fertilization strategies for plant nutrients might be improvable. Grape maturity estimation is mainly based on the development of sugar and acid concentrations, which both can be determined by NIR spectroscopy as demonstrated in various studies. The sugar concen- tration decides on the potential alcoholic strength while pH value and titratable acid help to control wine quality, e.g. red wine color (nogalES-BuEno et al., 2014). Further maturity parameters (maturi- ty index, various organic acids) were examined by NIR spectroscopy in grape berries at different stages of development (FErnándEz- novalES et al., 2009a; muSingaraBWi et al., 2016). In addition to these technological ripening parameters such as sugar and acid con- tent, the phenolic maturity of the grapes may play an important role for the resulting wine quality due to its influence on organoleptic properties (FErrEr-gallEgo et al., 2011). The anthocyanin contents were examined in grapes of different ripening times by the use of NIR spectroscopy. Although the contents could be determined re- 30 J. Gehlken, M. Pour Nikfardjam, M. Kleb, C. Zörb liably, a separation for the different ripening times only based on the anthocyanin content was not possible, probably due to different varieties examined (hErnándEz-hiErro et al., 2013). Portable de- vices for infield measurements can save time, because it is still a huge effort to sample enough grapes to achieve representative results for the whole vineyard (FErnándEz-novalES et al., 2019). Total soluble solids, anthocyanins and total polyphenols were examined using a device mounted on an all-terrain-vehicle (FErnándEz-novalES et al., 2019). The results show that the method has great potential for mapping the grape composition in the vineyard and harvest schedul- ing based on these results. Besides the technological and phenolic maturity, the phytosanitary status of the grapes is of particular importance for the quality of the resulting wine. One of the most common grape diseases is grape rot caused by Botrytis cinerea which negatively affects taste and color of the resulting wine (moralES-vallE et al., 2011) (except of “noble rot“) and can also affect food safety (SErra et al., 2005). Both in the vineyard and at delivery at the winery the phytosanitary status of the grapes is estimated visually, which makes the results subjective. NIR spectroscopy was shown to be suitable to quantify Botrytis bunch rot, although further calibration is needed (hill et al., 2014). Direct ap- plication of NIR technology in the vineyard would be advantageous, such as a device mounted on a grape harvester for sorting purposes. A real-time analysis of crushed grapes upon delivery at wineries was executed for the determination of ten different parameters (porEp et al., 2015a). The prediction accuracy of a global calibration model was insufficient for quantitative purposes (0.2 < R2 < 0.7), but could be improved by the development of separate calibration models for different grape colors, vintages and wineries. Commonly, glycerol, gluconic acid and acetic acid contents are used to receive informa- tion about the phytosanitary status of the grape (Sturm, 2009). Ergosterol, a sterol occurring in fungi but not in plant or animal tis- sue, was determined in grape mashes to prove the suitability of NIR spectroscopy for the detection of grape rot (porEp et al., 2014). Good results were achieved (R2 = 0.841 for cross-validation), which allow at least a semi-quantitative determination. In-line determination un- der industrial conditions confirmed the results (porEp et al., 2015b). A real-time objective method for wineries may further improve wine quality and safety and moreover enables fair pricing of delivered grapes due to their phytosanitary status. Wine fermentation is a very complex process, which requires de- tailed knowledge about the chemical composition of the fermenting mixture. For a non-viscous fermentation process, the content of yeast assimilable nitrogen plays an important role in avoiding sluggish or stuck fermentations (gardnEr et al., 2002). The grape must or mash undergoes many changes during fermentation, e.g. degradation of sugar and formation of ethanol. NIR spectroscopy can be used in white grape must to pre-estimate the potential alcoholic strength and the wine quality due to the contents of sugar and acid (Wu et al., 2010). Time-related changes in the fermentation process can be monitored, which enables to classify different fermentation stages (cozzolino et al., 2006a) and to predict the stage of fermentation due to the concentration of different compounds such as sugars, alcohols and secondary metabolites (di Egidio et al., 2010). The whole fermentation process can be monitored and critical points can be identified (Buratti et al., 2011), also regarding further treatment of the wine, such as the calcium content in must and ground wine for sparkling wine production (véStia et al., 2019). During wine pro- duction some by-products remain, such as grape pomace, which is rich in phenolic compounds (kammErEr et al., 2004). NIR spectros- copy can be used to easily determine its content, so grape pomace may be used as a natural source for extracting phenolic compounds for further use i.e. in bioeconomy instead of disposing of it direct- ly (páScoa et al., 2015). For the resulting wines, the chemical composition can be examined similarly. In an extensive study 15 parameters in red, white and rosé wines of different ages and from different origins were deter- mined (urBano-cuadrado et al., 2004). With the intention to fa- cilitate the quality evaluation of wines, different types of wines were measured in their bottles (cozzolino et al., 2007). Even though no exact quantitative evaluation was possible, however, the technique is suitable for qualitative evaluation and screening. An important aspect for the control of wine quality is the verification of the geographic origin, which is closely linked to climate, fertilization and soil cha- racteristics affecting quality and taste of the wine (FiSchEr et al., 1999). By the use of NIR spectroscopy, wines from different coun- tries could be differentiated with higher accuracy than wines from different regions in the same country, which may be caused by more similar growing conditions within the same country (riovanto et al., 2011). The contents of stable isotopes of mineral elements can serve as indicators for authenticity, but cannot be determined by NIR spec- troscopy because no specific isotope absorption is present in the NIR spectrum. Indirect estimation might only be possible due to asso- ciation with organic functional groups (cozzolino et al., 2008c). Furthermore, the variety of a wine is an important aspect since it is linked to sensory and chemical characteristics. Grape varieties could successfully be differentiated by NIR spectroscopy (cozzolino et al., 2003). Moreover, wines made from a single variety may be distin- guished from blends. For the determination of the single varieties in blended wines difficulties may occur for low contents and with an increasing number of varieties in the blend. Wine aroma and flavor are very complex issues, which are influenced by a large number of aspects, among them the selected yeast strain and bacteria (Boido et al., 2009). Different Oenococcus oeni strains for malolactic fermentation in red and white wines could be differen- tiated by NIR spectroscopy (cozzolino et al., 2012). Depending on the bacteria strain, classification rates up to 100% could be obtained. This way, potentially useful commercial wine strains can be identi- fied, which cause different aroma and flavor profiles. In grape and wine analysis, NIR spectroscopy is widely used with promising results. Main compounds like ethanol or sugar content can be determined with very high accuracy. For components being present in lower or trace amounts, such as polyphenolic compounds, it is difficult to obtain exact results, because their concentration is probably too small to affect the NIR spectrum significantly. Indirect determination is possible with good results due to correlation with compounds in higher concentration, but model robustness might be low for different batches (nicolaï et al., 2007). Problems may also occur due to interferences (martElo-vidal and vázquEz, 2014b, 2015). Furthermore, differentiation between various attributes showed difficulties in some cases with NIR spectroscopy giving only medium results. Applications in the evaluation of wine aroma The decision to buy a wine is based on outer influences, e.g. design of the bottle, name, variety, marketing or recommendations. For the motivation to buy the same wine again, the most important aspect is the taste and the aroma. The personal impression always remains subjective, although individual wines are closely linked to certain taste and flavor attributes depending on different characteristics like grape variety or wine style (cayuEla et al., 2017). The large number of compounds contributing to wine aroma and their low concentra- tions require very complex analyses. The most current method for the analysis of aroma compounds is gas-chromatography, often coupled with mass spectrometry as a detector (GC-MS). This way, many dif- ferent classes of compounds can be analyzed precisely even if they only occur in trace amounts. Tab. 3 gives an overview of the applications in grape and wine aroma evaluation using NIR spectroscopy. To obtain an objective opinion on NIR spectroscopy in process and quality control 31 Tab. 2: Applications of NIR spectroscopy in grape and wine analysis Parameters R2 Reference Soluble solids contenta 0.94 hErrEra et al., 2003 Reducing sugar content 0.98 FErnándEz-novalES et al., 2009b Total soluble solidsa 0.91 urraca et al., 2016 Titratable acida 0.86 chauchard et al., 2004 Soluble solids contenta (berries/bunches) 0.66/0.65 giovEnzana et al., 2015 Titratable acida 0.85 Sugar concentration 0.99 nogalES-BuEno et al., 2014 Titratable acid 0.98 pH value 0.94 Total phenolic content 0.89 Soluble solids contenta 0.92 riBEra-FonSEca et al., 2016a Titratable acida 0.87 Firmnessa 0.89 Total anthocyaninsa 0.94 Monomeric anthocyaninsa 0.68 – 0.96 Dry matter 0.88 cozzolino et al., 2008b Condensed tannins 0.81 Total anthocyanins 0.90 Janik et al., 2007 Total phenolic content (grapes/grape skins) 0.98/0.98 FErrEr-gallEgo et al., 2011 Flavonols (grapes/grape skins) 0.93/0.99 Flavanols (grapes/grape skins) 0.97/0.99 Phenolic acids (grapes/grape skins) 0.75/0.94 Anthocyanins (grapes/grape skins) 0.95/0.97 Total anthocyanins 0.86 hErnándEz-hiErro et al., 2013 Non-acylated anthocyanins 0.86 Total phenolic content (grape seeds) 0.53 – 0.78 rollE et al., 2012 Extractability 0.50 Extractable total phenolic content 0.82 nogalES-BuEno et al., 2015 Extractable flavanols 0.82 Extractable anthocyanins 0.79 Total and individual anthocyanins 0.35 – 0.90 diago et al., 2016 MCP Tannins 0.67 – 0.83 alEixandrE-tudo et al., 2019 Anthocyanins 0.50 – 0.93 Total phenols 0.61 – 0.90 Colour density 0.59 – 0.94 Soluble solids content (grape juice) 0.98 Wu et al., 2010 pH value (grape juice) 0.95 Total yeast assimilable nitrogen 0.96 pEtrovic et al., 2020 Free amino nitrogen 0.91 Ammonia 0.88 Volumic mass 0.99 FErnándEz-novalES et al., 2008 Reducing sugars 0.99 Volumic mass 0.94 FErnándEz-novalES et al., 2011b Total polyphenol index 0.98 Colour intensity 0.98 Volumic mass 0.98 FErnándEz-novalES et al., 2011a Malvidin-3-glucoside 0.81 – 0.94 cozzolino et al., 2004 Pigmented polymers 0.82 – 0.94 Tannins 0.70 – 0.90 Total phenolic content (grape pomace, raw/milled) 0.93/0.96 páScoa et al., 2015 Total antioxidant capacity (grape pomace, raw/milled) 0.91/0.97 Ethanol 0.67 cozzolino et al., 2007 pH value 0.50 Free SO2 0.83 Total SO2 0.70 Polyphenolic compounds 0.41 – 1 martElo-vidal and vázquEz, 2014a G ra pe P ar am et er s F or D et er m in in g M at ur it y W in e F er m en ta ti on W in e C om po un ds 32 J. Gehlken, M. Pour Nikfardjam, M. Kleb, C. Zörb Sodium 0.92; 0.77 SauvagE et al., 2002 Potassium 0.89; 0.89 cozzolino et al., 2008c Magnesium 0.92; 0.73 Calcium 0.91; 0.90 Phosphorus -; 0.79 Sulfur -; 0.70 Iron -; 0.86 Manganese -; 0.78 Boron -; 0.81 Grape variety 100/96b cozzolino et al., 2003 Geographic origin 93.5b liu et al., 2006 78b liu et al., 2008 76b cozzolino et al., 2011 60b riovanto et al., 2011 60.4b tEixEira doS SantoS et al., 2017 95/87/84b hu et al., 2019 a Portable NIR spectrometer used b Classification rate [%] sensory characteristics of wines, evaluation needs to be carried out by a sensory panel consisting of specially trained experts. Sensory evaluations are time-consuming and despite the skills of the judging persons the results remain subjective. NIR spectroscopy has been tested as an alternative method. It could be shown that some relation- ship exists between the spectra and the sensory properties of a wine, even without knowledge about chemical compounds (cozzolino et al., 2005). The results also showed that NIR spectroscopy is a suitable method in addition to sensory evaluation (cayuEla et al., 2017). However, total substitution of sensory evaluation by instru- mental analysis is not possible because the concentration of aroma compounds does not necessarily correlate with their contribution to the overall aroma, a factor, which is not sensed by instruments. Apart from sensory characteristics, classes or single aroma com- pounds in wines can be analyzed by NIR spectroscopy. The detec- tion and determination of desired aroma compounds as well as of those causing off-flavors have been examined. Despite their low concentrations, high prediction accuracies were reached in various studies (cynkar et al., 2007; Smyth et al., 2008; gEniShEva et al., 2018; FuEntES et al., 2019). In some cases, the prediction accuracy of the calibration models was good to excellent while in the vali- dation it was considerably worse, but were improved by the separa- tion of the sample set (lorEnzo et al., 2009; gardE-cErdán et al., 2010; gardE-cErdán et al., 2012). Nonetheless, NIR spectroscopy showed the potential to be used at least as a screening method. Most of the evaluations in Tab. 3 have been done for sensory attri- butes or aroma compounds in wine. Few examinations were done regarding the aroma of grapes intended for winemaking (cynkar et al., 2007; Boido et al., 2013), which has a great influence on the aroma of the resulting wine and therefore represents an important quality prediction parameter. Many volatile compounds, which con- tribute to the aroma of the resulting wine are accumulated in the grapes as non-volatile glycosylated compounds but are released dur- ing winemaking and storage (aroma potential) (Wirth et al., 2001). An early determination of the aroma potential can be helpful to decide about the best treatment of the grapes during winemaking. Although glycosylated compounds are important, they only repre- sent a part of the grape aroma and its role in the resulting wine aroma. The determination of the whole aroma profile of grapes by NIR spectroscopy would provide a helpful tool to sort grapes be- fore winemaking depending based on their aroma quality. Moreover, all these studies have been carried out under laboratory conditions, Tab. 3: Applications of NIR spectroscopy in the evaluation of grape and wine aroma Samples Compounds/Attributes R2 Reference Chardonnay and Riesling wines Sensory attributes 0.68 – 0.86 (r) cozzolino et al., 2005 Overall flavor 0.56 (r) Sweetness 0.60 (r) Riesling wines Sensory attributes 0.38 – 0.89 (r) cozzolino et al., 2006b Red and white wines Sensory attributes 0.73 – 0.93 (r) cayuEla et al., 2017 Red wines Wine scores 0.65 (r) cozzolino et al., 2008a Tannat grapes Glycosylated compounds 0.27 – 0.82 Boido et al., 2013 White grape juice Glycosylated compounds 0.73 – 0.94 cynkar et al., 2007 Riesling wines Esters 0.74 Smyth et al., 2008 Short-chain fatty acids 0.80 Monoterpenes 0.90 White wines Alcohols / Esters 0.94 – 0.97 gEniShEva et al., 2018 Aged red wines Alcohols / Esters / Short chain fatty acids 0.29 – 0.94 lorEnzo et al., 2009 Barrel-aged red wines Oak volatile compounds / Ethylphenols 0.27 – 0.84 gardE-cErdán et al., 2010 Red and white grapes and wines Guaiacol glycoconjugates 0.97 (r) FuEntES et al., 2019 Barrel-aged red wines Haloanisols / Halophenols 0.22 – 0.99 gardE-cErdán et al., 2012 NIR spectroscopy in process and quality control 33 which may cause a loss of time in practical application due to ad- ditional sampling time. The development of an in-line NIR spec- troscopy measurement of grape aroma compounds would be a great progress for the evaluation of the aroma quality of grapes directly on arrival at the winery. Conclusions The suitability of NIR spectroscopy has been examined and shown in numerous studies on fruits, grapes, wine, and wine aroma. Miscellaneous compounds and attributes can be obtained from the NIR spectrum in various kinds of matrices. With an adequate cali- bration, the NIR spectroscopy provides a wide range of applications in process control and quality management (Fig. 1). The calibration represents the most important and the most time-consuming part in establishing NIR spectroscopy as an analytical method. dates quality at different stages of maturity utilising near-infrared (NIR) spectroscopy. Int. J. Food Prop. 20, 2950-2959. 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DOI: 10.1016/j.tplants.2018.08.012 ORCID Jana Gehlken https://orcid.org/0000-0002-4659-6116 Martin Pour Nikfardjam https://orcid.org/0000-0001-5055-3496 Melissa Kleb https://orcid.org/0000-0002-6431-950X Christian Zörb https://orcid.org/0000-0003-0000-5138 Address of the authors: Jana Gehlken, State Research Institute for Viticulture and Pomiculture, Traubenplatz 5, 74189 Weinsberg, Germany E-mail: Jana.Gehlken@lvwo.bwl.de Martin Pour Nikfardjam, State Research Institute for Viticulture and Pomiculture, Traubenplatz 5, 74189 Weinsberg, Germany E-mail: Martin.PourN@lvwo.bwl.de Melissa Kleb, Institute of Crop Science, Quality of Plant Products (340e), University of Hohenheim, Schloss Westflügel, 70599 Stuttgart, Germany E-mail: Melissa.Kleb@uni-hohenheim.de Christian Zörb, Institute of Crop Science, Quality of Plant Products (340e), University of Hohenheim, Schloss Westflügel, 70599 Stuttgart, Germany E-mail: Christian.Zoerb@uni-hohenheim.de © The Author(s) 2021. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creative- commons.org/licenses/by/4.0/deed.en). http://dx.doi.org/10.1016/j.tplants.2018.08.012