Detection of low-level gluten content in flour and batter by near infrared reflectance spectroscopy (NIRS)

Albanell, Elena; Miñarro, Begoña; Carrasco, Noemí

doi:10.1016/j.jcs.2012.06.011

Cited by 33 publications

(24 citation statements)

References 25 publications

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“…In that study, NIR (1100-2000 nm) was used to scan wheat grain and flour samples, and classification models were built using partial least squares discriminant analysis (PLSDA), with prediction accuracies of 90-96%. The authentication of various powder samples has been performed using NIR spectroscopy [22][23][24][25][26]. Cocchi et al [23] utilised a Vis/NIR system (400-2500 nm) to identify traces of bread wheat flour in durum wheat flour using soft independence modelling of class analogy (SIMCA) classification models.…”

Section: Nir Technology Applications In the Food Manufacturingmentioning

confidence: 99%

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: Nir Technology Applications In the Food Manufacturingmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…In a similar investigation, Soares et al 23 were able to predict the protein concentration with a maximal error of 0.18 % by using a genetic algorithm instead of the ant colony algorithm. NIR spectra were used to find low concentrations of gluten in flour and doughs by Albanell et al 24. For both flour and dough, determination coefficients > 0.90 were achieved, but their method is still not able to identify gluten‐free products.…”

Section: Near‐infrared (Nir) Spectroscopymentioning

confidence: 99%

Supervision of Food Manufacturing Processes Using Optical Process Analyzers – An Overview

et al. 2016

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The food industry is the fourth largest industrial sector in Germany. The eagerness for innovation is classified as low. The food industry faces significantly larger challenges compared to the chemical industry since the demands of raw materials on processing are higher and more complex. In this contribution, the characteristics of food manufacturing are presented. The potential of optical process analyzers based on NIR, fluorescence, and Raman spectroscopy as well as on digital image analysis is demonstrated. These process analyzers can provide important information on raw materials, intermediate and end products, and improve the automation grade of production processes.

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“…Over the last decade, NIRS has gained popularity in the grain processing industry to assist with standardization in several parts of the world (Pierna et al, 2012;Salgó and Gergely, 2012;Albanell et al, 2012). According to Williams (1975), the Canadian Grain Commission adopted NIRS as their official analytic method for proteins and detection of adulterants in wheat in the 1970s.…”

Section: Near-infrared Spectroscopy (Nirs)mentioning

confidence: 99%

A Review of Technologies for Detection and Measurement of Adulterants in Cereals and Cereal Products

Ambrose¹,

Cho²

2014

Journal of Biosystems Engineering

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Purpose:The continued increase in the world population has triggered an increased demand for food. Cereal grains, flour, and their products constitute the staple diet for most of the world's population. This high demand for food, particularly for cereal-based products, has been exploited for commercial gain through adulteration of food materials. We provide a thorough review of the current developments and limitations of modern, nondestructive analytical techniques used for detection of adulterants in cereals and their products and compare them with conventional methods. Results: Adulterated food poses a serious health risks to humans, animals, and the ecosystem in general. Over the last few decades, the adulteration industry has developed fraudulent practices that often outsmart conventional methods of detection and quality control. Therefore, technological advancements to aid in detection and measurement of adulterants in food products and to ensure food quality and safety are critically important to consumers worldwide. Conclusion: There is a continuous demand for development of nondestructive technology to improve the accuracy and efficiency of detection, measurement, and qualification of adulterants in cereals and other food materials.

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Detection of low-level gluten content in flour and batter by near infrared reflectance spectroscopy (NIRS)

Cited by 33 publications

References 25 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

Supervision of Food Manufacturing Processes Using Optical Process Analyzers – An Overview

A Review of Technologies for Detection and Measurement of Adulterants in Cereals and Cereal Products

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