Procedures of Food Quality Control: Analysis Methods, Sampling and Sample Pretreatment

Ornelas‐Soto, Nancy; Barbosa-Garcı́a, Oracio; López-de-Alba, Pedro Luı́s

doi:10.5772/23206

Cited by 3 publications

(3 citation statements)

References 116 publications

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“…Although NIR technologies have extensive capabilities for measuring various quality parameters of food products in production environments, there are numerous challenges that must be addressed. Ensuring a representative sample of the material to be measured is a critical factor often affecting the features of the acquired spectra and, consequently, the performance of any classification or regression models developed [31]. Moreover, in powder foods, variations in physical properties (e.g., particle size) will significantly affect the chemical information identified by the NIR system [31].…”

Section: Limitations Of Online Applications Of the Nir Technologymentioning

confidence: 99%

“…Ensuring a representative sample of the material to be measured is a critical factor often affecting the features of the acquired spectra and, consequently, the performance of any classification or regression models developed [31]. Moreover, in powder foods, variations in physical properties (e.g., particle size) will significantly affect the chemical information identified by the NIR system [31]. In the case of diffuse reflectance NIR techniques, light penetrates and scatters inside the material before being reflected back and received by the detector [32].…”

Section: Limitations Of Online Applications Of the Nir Technologymentioning

confidence: 99%

“…Examples of the sample materials in petri dishes are shown in Figure 1a. It should be noted that the tested samples included powders, flakes, and seeds, which have different shapes and would produce differences in the recorded spectra [13,31]. Although some samples could easily be identified by their visible features (colour and shape), this was not possible for many, especially the flours (Figure 1a).…”

Section: Raw Materialsmentioning

confidence: 99%

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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Rady

Fischer

Reeves

et al. 2019

Sensors

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Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.

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Section: Limitations Of Online Applications Of the Nir Technologymentioning

confidence: 99%

Section: Limitations Of Online Applications Of the Nir Technologymentioning

confidence: 99%

Section: Raw Materialsmentioning

confidence: 99%

See 1 more Smart Citation

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Rady

Fischer

Reeves

et al. 2019

Sensors

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Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

et al. 2021

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Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).

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Features of Arranging of Laboratory Quality Control of Public Catering Products

Гращенков¹,

Мажаева²

2019

PH&LE

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A description of some modern approaches to the arranging of laboratory quality tests of public catering products is given. The object of the study was food raw materials and food products, as well as products of public catering enterprises, which were subjected to physical and chemical tests.We researched to analyze the existing system of arranging of laboratory quality tests and new approaches and recommendations development for its improvement.

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Procedures of Food Quality Control: Analysis Methods, Sampling and Sample Pretreatment

Cited by 3 publications

References 116 publications

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors

Features of Arranging of Laboratory Quality Control of Public Catering Products

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