scholarly journals Quantification of Total Phenolic and Carotenoid Content in Blackberries (Rubus Fructicosus L.) Using Near Infrared Spectroscopy (NIRS) and Multivariate Analysis

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3191 ◽  
Author(s):  
Eva Toledo-Martín ◽  
María García-García ◽  
Rafael Font ◽  
José Moreno-Rojas ◽  
María Salinas-Navarro ◽  
...  

A rapid method to quantify the total phenolic content (TPC) and total carotenoid content (TCC) in blackberries using near infrared spectroscopy (NIRS) was carried out aiming to provide reductions in analysis time and cost for the food industry. A total of 106 samples were analysed using the Folin-Ciocalteu method for TPC and a method based on Ultraviolet-Visible Spectrometer for TCC. The average contents found for TPC and TCC were 24.27 mg·g−1 dw and 8.30 µg·g−1 dw, respectively. Modified partial least squares (MPLS) regression was used for obtaining the calibration models of these compounds. The RPD (ratio of the standard deviation of the reference data to the standard error of prediction (SEP)) values from external validation for both TPC and TCC were between 1.5 < RPDp < 2.5 and RER values (ratio of the range in the reference data to SEP) were 5.92 for TPC and 8.63 for TCC. These values showed that both equations were suitable for screening purposes. MPLS loading plots showed a high contribution of sugars, chlorophyll, lipids and cellulose in the modelling of prediction equations.

Molecules ◽  
2017 ◽  
Vol 22 (5) ◽  
pp. 851 ◽  
Author(s):  
Eva Toledo-Martín ◽  
Rafael Font ◽  
Sara Obregón-Cano ◽  
Antonio De Haro-Bailón ◽  
Myriam Villatoro-Pulido ◽  
...  

2006 ◽  
Vol 46 (5) ◽  
pp. 605
Author(s):  
M. R. Fleet ◽  
L. Li ◽  
Y. Ru

Increased crossbreeding of Merino sheep in Australia, involving coloured or highly medullated sire breeds, has increased the risk of dark and highly medullated fibres in wool lots. Commercial implementation of the Dark and Medullated Fibre Risk Scheme, based on producer information, is identifying to buyers some of these risks and technology is sought to provide an inexpensive method for routine presale testing of greasy wool lots. One measurement concept assessed the ability of near infrared spectroscopy (NIRS) to predict variation in levels of pigmented fibres or highly medullated fibres in wool. The project used either ‘seeded’ wool samples or naturally contaminated samples with measured reference values as well as different methods of sample preparation of wool fibre (in air or immersed in benzyl alcohol) or the solutions from alkali hydrolysis of wool fibre and NIRS measurement (reflectance v. transmission). NIRS can predict variation in trace levels of pigmented fibre or highly medullated white fibres (kemp) in wool and, among the methods assessed, reflectance analysis of wool fibre in air was generally better than the other options considered. Calibration models for NIRS reflectance measurement of 5 g wool samples ‘seeded’ with 1–50 black-pigmented, tan-pigmented or white kemp fibres gave coefficients of determination (R2) of 0.96, 0.88 and 0.82 with standard errors of cross-validation (SECV) of 4.34, 6.97 and 7.17 fibres per 5 g sample, respectively. However, these calibration equations generally did not predict variations in the reference values for 3 other sets of naturally contaminated samples. New calibration equations determined for each of the sets of naturally contaminated samples also predicted variation in the pigmented fibre reference values, with the extent of agreement depending on the accuracy of the reference data as well as sample preparation and method of measurement. Calibration models for NIRS reflectance measurement of wool fibre from the 3 sets of naturally contaminated samples produced R2 = 0.99, 0.71 and 0.92 with SECV = 0.923, 6.43 and 4.54 pigmented fibres per 5 g sample, respectively. However, these calibrations and those obtained from various combinations of the wool sets also had limited ability to predict variation in pigmented fibre reference values in other independent or excluded samples. Refinement of the technique and development of calibrations with extensive and reliable reference data, representing all of the wool variation likely to be encountered, may allow this NIRS potential to become relevant in the presale testing of wool as an inexpensive measurement procedure for estimating dark and medullated fibre content.


2018 ◽  
Vol 26 (5) ◽  
pp. 275-286 ◽  
Author(s):  
Muhammad Arslan ◽  
Zou Xiaobo ◽  
Hu Xuetao ◽  
Haroon Elrasheid Tahir ◽  
Jiyong Shi ◽  
...  

Fourier-transform near infrared spectroscopy coupled with chemometric algorithms was applied comparatively for the quantification of chemical compositions in black wolfberry. The compositional parameters, i.e. total flavonoid content, total anthocyanin content, total carotenoid content, total sugar, and total acid were performed for quantification. Model results were evaluated using the correlation coefficients of determination for calibration (R2) and prediction (r2), root-mean-square error of prediction and residual predictive deviation. The findings revealed that the performances of models based on variable selection such as synergy interval-PLS, backward interval-PLS and genetic algorithm-PLS were better than the classical PLS. The performance of the developed models yielded 0.88 ≤ R2 ≤ 0.97, 0.87 ≤ r2 ≤ 0.94 and 1.75 ≤ RPD ≤ 4.00. The overall results showed that the FT-NIR spectroscopy in conjunction with chemometric algorithms could be used for the quantification of the chemical composition of black wolfberry samples.


Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1042
Author(s):  
Silvia Grassi ◽  
Olusola Samuel Jolayemi ◽  
Valentina Giovenzana ◽  
Alessio Tugnolo ◽  
Giacomo Squeo ◽  
...  

Poorly emphasized aspects for a sustainable olive oil system are chemical analysis replacement and quality design of the final product. In this context, near infrared spectroscopy (NIRS) can play a pivotal role. Thus, this study aims at comparing performances of different NIRS systems for the prediction of moisture, oil content, soluble solids, total phenolic content, and antioxidant activity of intact olive drupes. The results obtained by a Fourier transform (FT)-NIR spectrometer, equipped with both an integrating sphere and a fiber optic probe, and a Vis/NIR handheld device are discussed. Almost all the partial least squares regression models were encouraging in predicting the quality parameters (0.64 < R2pred < 0.84), with small and comparable biases (p > 0.05). The pair-wise comparison between the standard deviations demonstrated that the FT-NIR models were always similar except for moisture (p < 0.05), whereas a slightly lower performance of the Vis/NIR models was assessed. Summarizing, while on-line or in-line applications of the FT-NIR optical probe should be promoted in oil mills in order to quickly classify the drupes for a better quality design of the olive oil, the portable and cheaper Vis/NIR device could be useful for preliminary quality evaluation of olive drupes directly in the field.


2021 ◽  
Vol 3 (1) ◽  
pp. 73-91
Author(s):  
João Serrano ◽  
Shakib Shahidian ◽  
Ângelo Carapau ◽  
Ana Elisa Rato

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.


2016 ◽  
Vol 56 (9) ◽  
pp. 1504 ◽  
Author(s):  
J. P. Keim ◽  
H. Charles ◽  
D. Alomar

An important constraint of in situ degradability studies is the need to analyse a high number of samples and often with insufficient amount of residue, especially after the longer incubations of high-quality forages, that impede the study of more than one nutritional component. Near-infrared spectroscopy (NIRS) has been established as a reliable method for predicting composition of many entities, including forages and other animal feedstuffs. The objective of this work was to evaluate the potential of NIRS for predicting the crude protein (CP) and neutral detergent fibre (NDF) concentration in rumen incubation residues of permanent and sown temperate pastures in a vegetative stage. In situ residues (n = 236) from four swards were scanned for their visible-NIR spectra and analysed for CP and NDF. Selected equations developed by partial least-squares multivariate regression presented high coefficients of determination (CP = 0.99, NDF = 0.95) and low standard errors (CP = 4.17 g/kg, NDF = 7.91 g/kg) in cross-validation. These errors compare favourably to the average concentrations of CP and NDF (146.5 and 711.2 g/kg, respectively) and represent a low fraction of their standard deviation (CP = 38.2 g/kg, NDF = 34.4 g/kg). An external validation was not as successful, with R2 of 0.83 and 0.82 and a standard error of prediction of 14.8 and 15.2 g/kg, for CP and NDF, respectively. It is concluded that NIRS has the potential to predict CP and NDF of in situ incubation residues of leafy pastures typical of humid temperate zones, but more robust calibrations should be developed.


2017 ◽  
Vol 25 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Joseph Dubrovkin

It was shown that linear transformations are suitable for use in multivariate calibration in near infrared spectroscopy as data compression tools. Partial Least Squares calibration models were built using spectral data transformed by expansion in the series of classical orthogonal polynomials, Fourier and wavelet harmonics. These models allowed effective prediction of the cetane number of diesel fuels, Brix and pol parameters of syrup in sugar production and fat and total protein content in milk. Depending on the compression ratio, prediction errors were no larger than 30% of corresponding errors obtained by the use of the non-transformed models. Although selection of the most suitable transformation depends on the calibration data and on the cross-validation method, in many cases Fourier transform gave satisfactory results.


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