Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification.

Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.

Differentiation of Geographic Origin of South African Wines from Austrian Wines by IRMS and SNIF-NMR.

Geographic origin and terroir are very important parameters for wine and significantly impact price. Incorrect declarations are known to occur intentionally to increase profit, thus, measures for control are required. Accompanying paperwork has been shown to be unreliable, thus, control of the product itself is required. Here we investigate and compare the stable isotope pattern of South African (Western Cape Province) wine, and evaluate its potential for discrimination from Central European/Austrian wine. The results show that the isotope values of the investigated South African wine samples differ significantly from the values of average Austrian (Central European) wines. Thus, a differentiation of the products from these two regions by stable isotope analysis is generally straightforward. However, the data suggest that vintages from years with exceptionally hot and dry summer weather in Europe may reduce the differentiation between these regions. Therefore, this method is a potent tool for the discrimination of Austrian (Central European) and South African wines under current climatic conditions, although drier and hotter summer weather in Europe, which is likely to occur more frequently due to global climate change, may require further method adjustments in the future.

Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy.

The pomegranate kernel oil has gained global awareness due to the health benefits associated with its consumption; these benefits have been attributed to its unique fatty acid composition. For quality control of edible fats and oils, various analytical and calorimetric methods are often used, however, these methods are expensive, labor-intensive, and often require specialized sample preparation making them impractical on a commercial scale. Therefore, objective, rapid, accurate, and cost-effective methods are required. In this study, Fourier transformed near-infrared (FT-NIR) and mid-infrared (FT-MIR) spectroscopy as a fast non-destructive technique was investigated and compared to qualitatively and quantitatively predict the quality attributes of pomegranate kernel oil (cv. Wonderful, Acco, Herskawitz). For qualitative analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) was applied. Based on OPLS-DA, FT-MIR spectroscopy resulted in 100% discrimination between oil samples extracted from different cultivars. For quantitative analysis, partial least squares regression was used for model development over the NIR region of 7,498-940 and 6,102-5,774 cm-1 and provided the best prediction statistics for total carotenoid content (R 2, coefficient of determination; RMSEP, root mean square error of prediction; RPD, residual prediction deviation; R 2 = 0.843, RMSEP = 0.019 g ß-carotene/kg, RPD = 2.28). In the MIR region of 3,996-1,118 cm-1, models developed using FT-MIR spectroscopy gave the best prediction statistics for peroxide value (R 2 = 0.919, RMSEP = 1.05 meq, RPD = 3.54) and refractive index (R 2 = 0.912, RMSEP = 0.0002, RPD = 3.43). These results demonstrate the potential of infrared spectroscopy combined with chemometric analysis for rapid screening of pomegranate oil quality attributes.

Non-Invasive Methods for Predicting the Quality of Processed Horticultural Food Products, with Emphasis on Dried Powders, Juices and Oils: A Review.

This review covers recent developments in the field of non-invasive techniques for the quality assessment of processed horticultural products over the past decade. The concept of quality and various quality characteristics related to evaluating processed horticultural products are detailed. A brief overview of non-invasive methods, including spectroscopic techniques, nuclear magnetic resonance, and hyperspectral imaging techniques, is presented. This review highlights their application to predict quality attributes of different processed horticultural products (e.g., powders, juices, and oils). A concise summary of their potential commercial application for quality assessment, control, and monitoring of processed agricultural products is provided. Finally, we discuss their limitations and highlight other emerging non-invasive techniques applicable for monitoring and evaluating the quality attributes of processed horticultural products. Our findings suggest that infrared spectroscopy (both near and mid) has been the preferred choice for the non-invasive assessment of processed horticultural products, such as juices, oils, and powders, and can be adapted for on-line quality control. Raman spectroscopy has shown potential in the analysis of powdered products. However, imaging techniques, such as hyperspectral imaging and X-ray computed tomography, require improvement on data acquisition, processing times, and reduction in the cost and size of the devices so that they can be adopted for on-line measurements at processing facilities. Overall, this review suggests that non-invasive techniques have the potential for industrial application and can be used for quality assessment.

Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks.

Table grape browning is a complex physiological disorder that occurs during cold storage. There is a need to investigate novel and innovative ways to manage the problem that hampers the progressive and sustainable growth of table grape industries. Given the complex nature of the browning phenomenon, techniques such as near-infrared (NIR) spectroscopy can be utilized for the non-destructive classification of different browning phenotypes. In this study, NIR coupled with partial least squares discriminant analysis (PLS-DA) and artificial neural networks (ANN) were used to classify bunches as either clear or as having chocolate browning and friction browning based on the spectra obtained from intact 'Regal Seedless' table grape bunches that were cold-stored over different periods. Friction browning appears as circular spots close to the pedicel area that are formed when table grape berries move against each other, and chocolate browning appears as discoloration, which originates mostly from the stylar-end of the berry, although the whole berry may appear brown in severe instances. The evaluation of the models constructed using PLS-DA was done using the classification error rate (CER), specificity, and sensitivity and for the models constructed using ANN, the kappa score was used. The CER for chocolate browning (25%) was better than that of friction browning (46%) for weeks 3 and 4 for both class 0 (absence of browning) and class 1 (presence of browning). Both the specificity and sensitivity of class 0 and class 1 for friction browning were not as good as that of chocolate browning. With ANN, the kappa score was tested to classify table grape bunches as clear or having chocolate browning or friction browning and showed that chocolate browning could be classified with a strong agreement during weeks 3 and 4 and weeks 5 and 6 and that friction browning could be classified with a moderate agreement during weeks 3 and 4. These results open up new possibilities for the development of quality checks of packed table grape bunches before export. This has a significant impact on the table grape industry for it will now be possible to evaluate bunches non-destructively during packaging to determine the possibility of these browning types being present when reaching the export market.

Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs.

The fourth agricultural revolution is leading us into a time of using data science as a tool to implement precision viticulture. Infrared spectroscopy provides the means for rapid and large-scale data collection to achieve this goal. The non-invasive applications of infrared spectroscopy in grapevines are still in its infancy, but recent studies have reported its feasibility. This review examines near infrared and mid infrared spectroscopy for the qualitative and quantitative investigation of intact grapevine organs. Qualitative applications, with the focus on using spectral data for categorization purposes, is discussed. The quantitative applications discussed in this review focuses on the methods associated with carbohydrates, nitrogen, and amino acids, using both invasive and non-invasive means of sample measurement. Few studies have investigated the use of infrared spectroscopy for the direct measurement of intact, fresh, and unfrozen grapevine organs such as berries or leaves, and these studies are examined in depth. The chemometric procedures associated with qualitative and quantitative infrared techniques are discussed, followed by the critical evaluation of the future prospects that could be expected in the field.

Effect of Different Extraction Methods on the Quality and Biochemical Attributes of Pomegranate Juice and the Application of Fourier Transformed Infrared Spectroscopy in Discriminating Between Different Extraction Methods.

This study investigated the effects of extraction methods on the physicochemical, phytochemical, and antioxidant properties of pomegranate juice (cv. Wonderful). In addition, the application of attenuated total reflectance Fourier transformed mid-infrared (ATR-FT-MIR) spectroscopy and chemometrics were explored in order to discriminate between different extraction methods. Juice variants evaluated included juice extracted without crushing the seeds (arils only) using a juice extractor (JE), juice extracted by crushing the seeds using a blender (arils plus seed) (JB), and juice extracted from half fruit using a commercial hand press juicer (CH). Juice extracted from CH had higher total soluble solid (TSS) content (18.20%), TSS/TA ratio (15.83), and color properties (a* = 32.67, b* = 11.80, C* = 34.77) compared with extraction methods JE and JB. The juice extracted from JB showed the highest titratable acidity (2.17%), cloudiness (0.43), and lowest pH value (2.69). The total phenolics and anthocyanin content in the investigated juice ranged from 1.87 to 3.04 g gallic acid equivalent (GAE)/L and 37.74-43.67 mg cyanidin 3-glucoside equivalent/L of crude juice, respectively. Juice extracted from JB and CH was significantly higher in phenolic and anthocyanin compared with JE. Orthogonal partial least squares discriminant analysis (OPLS-DA) and principal component analysis (PCA) were used for classification. Classification accuracy of 100% was achieved between the three methods. The S-line plot revealed that the corresponding wavelength bands within the following regions 1,090, 1,250, 1,750, and 3,200 cm-1 were responsible for discrimination between the different extraction methods. Our results suggest that the main contributor to the discrimination between extraction methods were TSS, TSS/TA, color attributes, and anthocyanin content. Overall, this study has demonstrated that ATR-FT-MIR spectroscopy provides a powerful way to discriminate between juice extraction methods.

The Impact of "Wine Country of Origin" on the Perception of Wines by South African and French Wine Consumers: A Cross-Cultural Comparison.

Culture is an important factor that influences how marketing interacts with food choice. This study aims at exploring the effect of consumers' Country of Origin (COO) on wine representations and perception using Chenin blanc as a model. The first objective was to evaluate the role of origin in the construction of the representation. We used the theoretical framework of social representation to compare South African (SA) and French consumers' representations via a word association task. The results indicated that SA representations are dominated by consumers' experience of the wine (sensory and emotional dimensions), whereas French representations are dominated by the wine itself, in particular its origin and mode of consumption. The second objective was to evaluate the effect of origin on wine categorization in two conditions: with and without information concerning the two geographical origins of the samples. Results showed that providing information on the origin of the wines affected French participants more than SA participants. In both conditions, the groups of wines formed in the sorting tasks by SA participants were based on sensory descriptors and appeared not to be impacted by the information on origin. In contrast, providing information on the origin of the wines to French participants led to an increased use of the words "Loire", "South Africa" and "familiar" suggesting a different sorting strategy more deliberately based on the origin of the wines. Our findings have important implications for the marketing and export activities within the wine industry.

Measuring Internal Maturity Parameters Contactless on Intact Table Grape Bunches Using NIR Spectroscopy.

The determination of internal maturity parameters of table grape is usually done destructively using manual methods that are time-consuming. The possibility was investigated to determine whether key fruit attributes, namely, total soluble solids (TSS); titratable acidity (TA), TSS/TA, pH, and BrimA (TSS - k x TA) could be determined on intact table grape bunches using Fourier transform near-infrared (FT-NIR) spectroscopy and a contactless measurement mode. Partial Least Squares (PLS) regression models were developed for the maturity and sensory quality parameters using grapes obtained from two consecutive harvest seasons. Statistical indicators used to evaluate the models were the number of latent variables (LVs) used to build the model, the prediction correlation coefficient (R2p) and root mean square error of prediction (RMSEP). For the respective parameters TSS, TA, TSS/TA, pH, and BrimA, the LVs were 21, 23, 5, 7, and 24, the R2p = 0.71, 0.33, 0.57, 0.28, and 0.77, and the RMSEP = 1.52, 1.09, 7.83, 0.14, and 1.80. TSS performed best when moving smoothing windows (MSW) + multiplicative scatter correction (MSC) was used as spectral pre-processing technique, TA with standard normal variate (SNV), TSS/TA with Savitzky-Golay first derivative (SG1d), pH with SG1d, and BrimA with MSC. This study provides the first steps towards a completely nondestructive and contactless determination of internal maturity parameters of intact table grape bunches.

Towards on-line monitoring of phenolic content in red wine grapes: A feasibility study.

Spectroscopy techniques to efficiently measure phenolic composition in grape berries may be a suitable analytical practice, provided that robust calibrations are established. A contactless FT-NIR instrument was used for on-line spectral data collection from grapes transported on a conveyor belt system. Spectral data was also collected on static samples using the same NIR instrument. Spectral measurements of crushed berries captured from the conveyor belt system and the use of the homogenate extraction protocol as reference method provided the most accurate prediction models. Values obtained for errors in prediction (RMSEP%) and RPD were 12% and 2.37, 12.3% and 3.37, 7.8% and 3.2, 16.7% and 2.84 for tannins (mg/g) and anthocyanins (mg/g) on a fresh weight basis, total phenols and colour density (AU), respectively. The results observed in this study show the ability of NIR spectroscopy to monitor the phenolic composition of grape berries transported on a conveyor belt system online.

Model development for non-destructive determination of rind biochemical properties of 'Marsh' grapefruit using visible to near-infrared spectroscopy and chemometrics.

Rind biochemical properties play major roles in defence mechanisms against the incidence of rind physiological disorders of citrus fruit during cold storage. Hence, multivariate calibration models were developed to rapidly and non-destructively determine rind biochemical properties of citrus fruit from visible to near-infrared (Vis/NIR) spectra acquired by Vis/NIR spectroscopy using partial least square regression algorithm. To achieve optimum models for determination of each rind biochemical property, several mathematical pre-processing methods were explored, including no pre-treatment. However, special emphases were given to the best model statistics in terms of coefficient of determination (R2) and residual predictive deviation (RPD). Models were performed by critical examination of different wavelength ranges (visible, near-infrared and full regions) and combinations of fruit harvested from different production regions and acquired before (week 0) and after (week 9) cold storage. Results obtained showed excellent models for determining parameters such as sucrose (R2 = 0.99 and RPD = 11.42), total flavonoids (R2 = 0.99 and RPD = 12.37), and chlorophyll b (R2 = 0.97 and RPD = 5.67). This study reported the first application of Vis/NIR and chemometrics in determining the rind biochemical properties of 'Marsh' grapefruit rapidly and non-destructively.

Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns.

The increasingly large volumes of publicly available sensory descriptions of wine raises the question whether this source of data can be mined to extract meaningful domain-specific information about the sensory properties of wine. We introduce a novel application of formal concept lattices, in combination with traditional statistical tests, to visualise the sensory attributes of a big data set of some 7,000 Chenin blanc and Sauvignon blanc wines. Complexity was identified as an important driver of style in hereto uncharacterised Chenin blanc, and the sensory cues for specific styles were identified. This is the first study to apply these methods for the purpose of identifying styles within varietal wines. More generally, our interactive data visualisation and mining driven approach opens up new investigations towards better understanding of the complex field of sensory science.

Chemometric compositional analysis of phenolic compounds in fermenting samples and wines using different infrared spectroscopy techniques.

The wine industry requires reliable methods for the quantification of phenolic compounds during the winemaking process. Infrared spectroscopy appears as a suitable technique for process control and monitoring. The ability of Fourier transform near infrared (FT-NIR), attenuated total reflectance mid infrared (ATR-MIR) and Fourier transform infrared (FT-IR) spectroscopies to predict compositional phenolic levels during red wine fermentation and aging was investigated. Prediction models containing a large number of samples collected over two vintages from several industrial fermenting tanks as well as wine samples covering a varying number of vintages were validated. FT-NIR appeared as the most accurate technique to predict the phenolic content. Although slightly less accurate models were observed, ATR-MIR and FT-IR can also be used for the prediction of the majority of phenolic measurements. Additionally, the slope and intercept test indicated a systematic error for the three spectroscopies which seems to be slightly more pronounced for HPLC generated phenolics data than for the spectrophotometric parameters. However, the results also showed that the predictions made with the three instruments are statistically comparable. The robustness of the prediction models was also investigated and discussed.

Spectrophotometric Analysis of Phenolic Compounds in Grapes and Wines.

Phenolic compounds are of crucial importance for red wine color and mouthfeel attributes. A large number of enzymatic and chemical reactions involving phenolic compounds take place during winemaking and aging. Despite the large number of published analytical methods for phenolic analyses, the values obtained may vary considerably. In addition, the existing scientific knowledge needs to be updated, but also critically evaluated and simplified for newcomers and wine industry partners. The most used and widely cited spectrophotometric methods for grape and wine phenolic analysis were identified through a bibliometric search using the Science Citation Index-Expanded (SCIE) database accessed through the Web of Science (WOS) platform from Thompson Reuters. The selection of spectrophotometry was based on its ease of use as a routine analytical technique. On the basis of the number of citations, as well as the advantages and disadvantages reported, the modified Somers assay appears as a multistep, simple, and robust procedure that provides a good estimation of the state of the anthocyanins equilibria. Precipitation methods for total tannin levels have also been identified as preferred protocols for these types of compounds. Good reported correlations between methods (methylcellulose precipitable vs bovine serum albumin) and between these and perceived red wine astringency, in combination with the adaptation to high-throughput format, make them suitable for routine analysis. The bovine serum albumin tannin assay also allows for the estimation of the anthocyanins content with the measurement of small and large polymeric pigments. Finally, the measurement of wine color using the CIELab space approach is also suggested as the protocol of choice as it provides good insight into the wine's color properties.

A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis.

Fourier transform (FT) near-infrared (NIR) and attenuated total reflection (ATR) FT mid-infrared (MIR) spectroscopy were used to qualitatively and quantitatively analyse Vitis vinifera L. cv Sauvignon blanc grape berries. FT-NIR and ATR FT-MIR spectroscopy, coupled with spectral preprocessing and multivariate data analysis (MVDA), provided reliable methods to qualitatively assess berry samples at five distinct developmental stages: green, pre-véraison, véraison, post-véraison and ripe (harvest), without any prior metabolite extraction. Compared to NIR spectra, MIR spectra provided more reliable discrimination between the berry samples from the different developmental stages. Interestingly, ATR FT-MIR spectra from fresh homogenized berry samples proved more discriminatory than spectra from frozen homogenized berry samples. Different developmental stages were discriminated by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). In order to generate partial least squares (PLS) models from the MIR/NIR spectral datasets; the major sugars (glucose and fructose) and organic acids (malic acid, succinic acid and tartaric acid) were separated and quantified by high performance liquid chromatography (HPLC) and the data used as a reference dataset. PLS regression was used to develop calibration models to predict the concentration of the major sugars and organic acids in the berry samples from different developmental stages. Our data show that infrared (IR) spectroscopy could provide a rapid, reproducible and cost-effective alternative to the chromatographic analysis of the sugar and organic acid composition of grape berries at various developmental stages, using small sample volumes and requiring limited sample preparation. This provides scope and support for the possible development of hand-held devices to assess quality parameters in field-settings in real-time and non-destructively using IR technologies.

A multivariate approach using attenuated total reflectance mid-infrared spectroscopy to measure the surface mannoproteins and ß-glucans of yeast cell walls during wine fermentations.

Yeast cells possess a cell wall comprising primarily glycoproteins, mannans, and glucan polymers. Several yeast phenotypes relevant for fermentation, wine processing, and wine quality are correlated with cell wall properties. To investigate the effect of wine fermentation on cell wall composition, a study was performed using mid-infrared (MIR) spectroscopy coupled with multivariate methods (i.e., PCA and OPLS-DA). A total of 40 yeast strains were evaluated, including Saccharomyces strains (laboratory and industrial) and non-Saccharomyces species. Cells were fermented in both synthetic MS300 and Chardonnay grape must to stationery phase, processed, and scanned in the MIR spectrum. PCA of the fingerprint spectral region showed distinct separation of Saccharomyces strains from non-Saccharomyces species; furthermore, industrial wine yeast strains separated from laboratory strains. PCA loading plots and the use of OPLS-DA to the data sets suggested that industrial strains were enriched with cell wall proteins (e.g., mannoproteins), whereas laboratory strains were composed mainly of mannan and glucan polymers.

Robust Ultraviolet-Visible (UV-Vis) Partial Least-Squares (PLS) Models for Tannin Quantification in Red Wine.

The validation of ultraviolet-visible (UV-vis) spectroscopy combined with partial least-squares (PLS) regression to quantify red wine tannins is reported. The methylcellulose precipitable (MCP) tannin assay and the bovine serum albumin (BSA) tannin assay were used as reference methods. To take the high variability of wine tannins into account when the calibration models were built, a diverse data set was collected from samples of South African red wines that consisted of 18 different cultivars, from regions spanning the wine grape-growing areas of South Africa with their various sites, climates, and soils, ranging in vintage from 2000 to 2012. A total of 240 wine samples were analyzed, and these were divided into a calibration set (n = 120) and a validation set (n = 120) to evaluate the predictive ability of the models. To test the robustness of the PLS calibration models, the predictive ability of the classifying variables cultivar, vintage year, and experimental versus commercial wines was also tested. In general, the statistics obtained when BSA was used as a reference method were slightly better than those obtained with MCP. Despite this, the MCP tannin assay should also be considered as a valid reference method for developing PLS calibrations. The best calibration statistics for the prediction of new samples were coefficient of correlation (R2val) = 0.89, root mean standard error of prediction (RMSEP) = 0.16, and residual predictive deviation (RPD) = 3.49 for MCP and R2val = 0.93, RMSEP = 0.08, and RPD = 4.07 for BSA, when only the UV region (260-310 nm) was selected, which also led to a faster analysis time. In addition, a difference in the results obtained when the predictive ability of the classifying variables vintage, cultivar, or commercial versus experimental wines was studied suggests that tannin composition is highly affected by many factors. This study also discusses the correlations in tannin values between the methylcellulose and protein precipitation methods.

Direct, simultaneous quantification of fructooligosaccharides by FT-MIR ATR spectroscopy and chemometrics for rapid identification of superior, engineered ß-fructofuranosidases.

Fructooligosaccharides (FOS) are popular components of functional foods produced by the enzymatic transfer of fructose units to sucrose. Improving ß-fructofuranosidase traits by protein engineering is restricted by the absence of a rapid, direct screening method for the fructooligosaccharide products produced by enzyme variants. The use of standard high-performance liquid chromatography (HPLC) methods involves time-consuming sample preparation and chromatographic and data analysis steps. To overcome these limitations, this work presents a rapid method for screening ß-fructofuranosidase variant libraries using Fourier transform mid-infrared attenuated total reflectance (FT-MIR ATR) spectroscopy and calibration using partial least squares (PLS) regression. The method offers notable improvements in terms of sample analysis times and cost, with the added benefit of the absence of toxic eluents. Wavenumber interval selection methods were tested to develop optimised PLS regression models that were successfully applied to quantify of glucose, fructose, sucrose, 1-kestose and nystose, the substrates and products of ß-fructofuranosidase activity. To the best of our knowledge, this is the first report on the use of infrared spectroscopy and PLS calibration for the quantification of 1-kestose and nystose. Independent test set-validated results indicated that optimal wavenumber selection by interval PLS (iPLS) served to provide the best models for all sugars, bar glucose. Application of this screening method will facilitate the engineering of ß-fructofuranosidases and other glycosyltransferase enzymes by random mutagenesis strategies, as it provides, for the first time, a rapid, direct assay for transferase products that may be adapted to a high-throughput set-up.

The use of Vis/NIRS and chemometric analysis to predict fruit defects and postharvest behaviour of 'Nules Clementine' mandarin fruit.

The use of chemometrics to analyse Vis/NIRS signal collected from intact 'Nules Clementine' mandarin fruit at harvest, to predict the rind physico-chemical profile after eight weeks postharvest was explored. Vis/NIRS signals of 150 fruit were obtained immediately after harvest. Reference data on the rind were obtained after eight-week storage, including colour index (CI), rind dry matter (DM), and concentration of sugars. Partial least squares (PLS) regression was applied to develop models. Principal component analysis (PCA) followed by PLS-discriminant analysis (PLS-DA) were used to classify fruit according to canopy position. Optimal PLS model performances for DM, sucrose, glucose and fructose were obtained using multiple scatter correction pre-processing, showing respective residual predictive deviation (RPD) of 3.39, 1.75, 2.19 and 3.08. Clusters of sample distribution in the PCA and PLS-DA models based on canopy position were obtained. The results demonstrated the potential applications of Vis/NIRS to predict postharvest behaviour of mandarin fruit.

The relationships between consumer liking, sensory and chemical attributes of Vitis vinifera L. cv. Pinotage wines elaborated with different Oenococcus oeni starter cultures.

BACKGROUND: Malolactic fermentation (MLF) mediated by lactic acid bacteria (LAB) has been shown to modulate chemical and sensory attributes of wine. This study investigated the relation between consumer liking, chemical and sensory attributes of Vitis vinifera L. cv. Pinotage wines that were made over two vintages by four different lactic acid Oenococcus oeni starter cultures as well as a control treatment where MLF was prevented. RESULTS: Descriptive analysis showed that the sensory attributes buttery, caramel, vegetative flavour, fruity and nutty aroma differed significantly between the wines. These effects on the wines were not the same for the two vintages tested. Preference mapping results showed that the sensory attributes influenced the average consumer liking. The main chemical and sensory correlations found for MLF-treated wines were related to 2,3-butanedione (diacetyl) with the buttery character and various esters with fruity aromas. CONCLUSION: Although the direct effect of the bacterial starter cultures on wine sensory attributes is difficult to establish, and subject to variation over vintage, the present work suggests that the contribution of LAB starter cultures to wine sensory attributes can influence consumer liking. Selection of an MLF starter culture can thus potentially be used to develop specific wine styles.