Quality control of cashew apple and guava nectar by near infrared spectroscopy

Caramês, Elem Tamirys dos Santos; Alamar, Priscila Domingues; Poppi, Ronei J.

doi:10.1016/j.jfca.2016.12.002

Cited by 30 publications

(13 citation statements)

References 29 publications

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“…Similarly, the technique has allowed for precise quantification of ascorbic acid contents in several fruits and fruit juices (Alamar et al., 2016; Andrianjaka‐Camps et al., 2015; Arendse et al., 2018; Blanco‐Díaz et al., 2014; Liu et al., 2015; Yang & Irudayaraj, 2002), and therefore, appears promising for extension toward the grape must and wine matrixes. Such an analysis could either be performed in the NIR–visible region of the spectrum, at wavelengths between 400 and 2500 nm (Alamar et al., 2016; Blanco‐Díaz et al., 2014; Caramês et al., 2017; Liu et al., 2015), or at MIR wavelengths, comprised between 2000 and 10,800 nm (Andrianjaka‐Camps et al., 2015; Arendse et al., 2018).…”

Section: Vitamin Analysis In Grape Musts and Winesmentioning

confidence: 99%

Vitamins in wine: Which, what for, and how much?

Evers

Roullier-Gall

Morge

et al. 2021

Comp Rev Food Sci Food Safe

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Vitamins are essential compounds to yeasts, and notably in winemaking contexts. Vitamins are involved in numerous yeast metabolic pathways, including those of amino acids, fatty acids, and alcohols, which suggests their notable implication in fermentation courses, as well as in the development of aromatic compounds in wines. Although they are major components in the course of those microbial processes, their significance and impact have not been extensively studied in the context of winemaking and wine products, as most of the studies focusing on the subject in the past decades have relied on relatively insensitive and imprecise analytical methods. Therefore, this review provides an extensive overview of the current knowledge regarding the impacts of vitamins on grape must fermentations, wine‐related yeast metabolisms, and requirements, as well as on the profile of wine sensory characteristics. We also highlight the methodologies and techniques developed over time to perform vitamin analysis in wines, and assess the importance of precisely defining the role played by vitamins in winemaking processes, to ensure finer control of the fermentation courses and product characteristics in a highly complex matrix.

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Section: Vitamin Analysis In Grape Musts and Winesmentioning

confidence: 99%

Vitamins in wine: Which, what for, and how much?

Evers

Roullier-Gall

Morge

et al. 2021

Comp Rev Food Sci Food Safe

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“…Numerous studies and publications have reported that the NIRS method has been able to predict and determine inner quality parameters of food, dairy products, and other agricultural products [9,10,11], such as meat, animal feed [10,12,13], horticulture [14,15,16,17], soil nutrients and properties [18,19]. This technique was discovered to be rapidly applied, effective, and non-destructive in determining several quality attributes of raw intact organic materials and other derivative products.…”

Section: Introductionmentioning

confidence: 99%

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Hayati

Zulfahrizal

Munawar

2021

Heliyon

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Fast and simultaneous determination of inner quality parameters, such as fat and moisture contents, need to be predicted in cocoa products processing. This study aimed to employ the near-infrared reflectance spectroscopy (NIRS) in predicting the quality mentioned above parameters in intact cocoa beans. Near-infrared spectral data, in a wavelength ranging from 1000 to 2500 nm, were acquired for a total of 110 bulk cocoa bean samples. Actual fat and moisture contents were measured with standard laboratory procedures using the Soxhlet and Gravimetry methods, respectively. Two regression approaches, namely principal component regression (PCR) and partial least square regression (PLSR), were used to develop the prediction models. Furthermore, four different spectra correction methods, namely multiple scatter correction (MSC), de-trending (DT), standard normal variate (SNV), and orthogonal signal correction (OSC), were employed to enhance prediction accuracy and robustness. The results showed that PLSR was better than PCR for both quality parameters prediction. Spectra corrections improved prediction accuracy and robustness, while OSC was the best correction method for fat and moisture content prediction. The maximum correlation of determination (R 2 ) and residual predictive deviation (RPD) index for fat content were 0.86 and 3.16, while for moisture content prediction, the R 2 coefficient and RPD index were 0.92 and 3.43, respectively. Therefore, NIRS combined with proper spectra correction method can be used to rapidly and simultaneously predict inner quality parameters of intact cocoa beans.

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“…Chemometrics, or computational chemistry, and mathematical and multivariate statistical tools (e.g., partial least-squares regression) and spectral pre-processing treatments are essential to extract the fruit quality relevant information from the convoluted VIS/NIR spectrum [8,[11][12][13]. VIS/NIR spectral data combined with chemometric analysis techniques have been applied to assess maturity, appearance, and sweetness of several agricultural produce, therefore ensuring that the produce meets specific quality standards [5,9,14].Harvest timing of table grapes and peaches is an important attribute, which has proven to be challenging to predict and/or estimate. Typically, fruit growers subjectively and visually estimate harvesting time by observing color changes on the fruit.…”

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confidence: 99%

Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

et al. 2020

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Near-infrared (NIR) spectroscopy has been used to non-destructively and rapidly evaluate the quality of fresh agricultural produce. In this study, two commercially available portable spectrometers (F-750: Felix Instruments, WA, USA; and SCiO: Consumer Physics, Tel Aviv, Israel) were evaluated in the wavelength range between 740 and 1070 nm to non-invasively predict quality attributes, including the dry matter (DM), and total soluble solids (TSS) content of three fresh table grape cultivars (‘Autumn Royal’, ‘Timpson’, and ‘Sweet Scarlet’) and one peach cultivar (‘Cassie’). Prediction models were developed using partial least-square regression (PLSR) to correlate the NIR absorbance spectra with the invasive quality measurements. In regard to grapes, the best DM prediction models yielded an R2 of 0.83 and 0.81, a ratio of standard error of performance to standard deviation (RPD) of 2.35 and 2.29, and a root mean square error of prediction (RMSEP) of 1.40 and 1.44; and the best TSS prediction models generated an R2 of 0.97 and 0.95, an RPD of 5.95 and 4.48, and an RMSEP of 0.53 and 0.70 for the F-750 and SCiO spectrometers, respectively. Overall, PLSR prediction models using both spectrometers were promising to predict table grape quality attributes. Regarding peach, the PLSR prediction models did not perform as well as in grapes, as DM prediction models resulted in an R2 of 0.81 and 0.67, an RPD of 2.24 and 1.74, and an RMSEP of 1.28 and 1.66; and TSS resulted in an R2 of 0.62 and 0.55, an RPD of 1.55 and 1.48, and an RMSEP of 1.19 and 1.25 for the F-750 and SCiO spectrometers, respectively. Overall, the F-750 spectrometer prediction models performed better than those generated by using the SCiO spectrometer data.

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Quality control of cashew apple and guava nectar by near infrared spectroscopy

Cited by 30 publications

References 29 publications

Vitamins in wine: Which, what for, and how much?

Vitamins in wine: Which, what for, and how much?

Robust prediction performance of inner quality attributes in intact cocoa beans using near infrared spectroscopy and multivariate analysis

Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes

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