On-line prediction of sodium content in vacuum packed dry-cured ham slices by non-invasive near infrared spectroscopy

Campos, M. Isabel; Mussons, Ma Luisa; Antolı́n, G.; Debán, Luis; Pardo, Rafael

doi:10.1016/j.meatsci.2016.12.005

Cited by 16 publications

(12 citation statements)

References 32 publications

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“…For quality parameters determination purposes, NIR spectroscopy could predict sodium content in commercially processed meat [ 97 ] and in vacuum dried ham slices [ 98 ]. Other parameters such as ash and dry matter in freeze-dried ostrich meat were also studied [ 99 ].…”

Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

“…Portable NIR spectrometers have been used for cost-saving but also for reliable monitoring of ham [ 98 ], beef [ 106 , 107 ], and routine analysis of Iberian pork [ 108 ]. Monounsaturated fatty acids, oleic acid, and saturated fatty acids could be predicted in wagyu beef carcass with R 2 of 0.79, 0.71, 0.81, respectively, when a handheld fiber optic spectrometer was used [ 108 ].…”

Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

See 1 more Smart Citation

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Aouadi

Zaukuu

Vitális

et al. 2020

Sensors

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Amid today’s stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

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Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

Section: Near Infrared (Nir) Spectroscopy: Historical Background Amentioning

confidence: 99%

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Aouadi

Zaukuu

Vitális

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…However these techniques are time consuming, require sample preparation, use of chemicals as well as trained operators (De Prados et al, 2015). Nondestructive technologies have been investigated to monitor the salting process including x-ray absorptiometry, microwave dielectric spectroscopy (Castro-Giráldez et al, 2010), computed tomography (Vestergaard et al, 2004), magnetic induction (Schivazappa et al, 2017), laser induced breakdown spectroscopy (Dixit et al, 2018) and NIR spectroscopy (Campos et al, 2017;Collell et al, 2011;Collell et al, 2012;Gaitán-Jurado et al, 2008). Application of any of these techniques has not been reported to date to monitor pork brining processes, to classify brined and non-brined pork or to predict brining salt concentration employed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

Achata

Inguglia

Esquerre

et al. 2019

Journal of Food Engineering

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Hyperspectral imaging in the visible and near infrared spectral range (450-1664 nm) coupled with chemometrics was investigated for classification of brined and non-brined pork loins and prediction of brining salt concentration employed. Hyperspectral images of control, water immersed and brined (5, 10 or 15% salt (w/v)) raw and cooked pork loins from 16 animals were acquired. Partial least squares (PLS) discriminative analysis models were developed to classify brined pork samples and PLS regression models were developed for prediction of brining salt concentration employed. The ensemble Monte Carlo variable selection method (EMCVS) was used to improve the performance of the models developed. Partial least squares (PLS) discriminative analysis models developed correctly classified brined and nonbrined samples, the best classification model for raw samples (Sen = 100%, Spec = 100%, G = 1.00) used the 957-1664 nm spectral range, and the best classification model for cooked samples (Sen = 100%, Spec = 100%, G = 1.00) used the 450-960 nm spectral range. The best brining salt concentration prediction models developed for raw (RMSE p 1.9%, R 2 p 0.92) and cooked (RMSE p 2.6%, R 2 p 0.83) samples used the 957-1664 nm spectral range. This study demonstrates the high potential of hyperspectral imaging as a process analytical tool to classify brined and non-brined pork loins and predict brining salt concentration employed.

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“…Near infrared (NIR) spectroscopy is a nondestructive technique that can evaluate chemical constituents rapidly. It has been used as a rapid and a nondestructive technique for measuring salt content of food such as meat, dry-cured ham, 8,9 processed meat product, 10 smoked salmon fillet portions, 11 and tomato juice. 12 In the case of dry-cured ham, 8 the samples were scanned in the wavelength range of 400-2500 nm.…”

Section: Introductionmentioning

confidence: 99%

“…The best model for salt content and salt percentage in moisture/dry matter was reported (r 2 CV > 0.90 and RPD > 3.0). Further, Campos et al 9 successfully applied the online prediction of the sodium content in dry-cured ham slices by NIR spectroscopy. Spectra were collected over the 830-2500 nm region.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy

Cheevitsopon

Sirisomboon

2018

Journal of Near Infrared Spectroscopy

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A feasibility study was performed to assess whether near infrared spectroscopy could evaluate the salt content of curry soup containing coconut milk. The soup samples were from the mixing tank, a water content adjusted tank, the ultra-high temperature pipe, and laminated containers of a food processor plant. In addition, fish sauce adjusted samples made from the same recipe but with increasing or decreasing (AE30%, 60%, and 90%) sauce content were prepared. There were 113 samples in total, which were scanned using a Fourier-transform near infrared spectrometer. The prediction models for salt content were established using near infrared spectral data in conjunction with partial least squares regression. Calibration models developed using all of the samples were validated using leave-one-out cross validation and test set validation. The unadjusted sample models were validated using test set validation. The results showed that both validation methods for the calibration models using all of the samples provided similar model performance where the r 2 , root mean square error of calibration/root mean square error of prediction, and residual predictive deviation were 0.956, 0.065%, and 4.77 for cross validation and 0.954, 0.064%, and 4.64 for the test set, respectively. However, the salt unadjusted sample model showed better performance where the r 2 , RMSEP, and RPD were respectively 0.963, 0.043%, and 5.23, indicating that excellent models can be developed to determine the salt content of curry soup containing coconut milk for any applications, including quality assurance.

show abstract

On-line prediction of sodium content in vacuum packed dry-cured ham slices by non-invasive near infrared spectroscopy

Cited by 16 publications

References 32 publications

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue—Critical Overview

Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration

Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy

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