The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study

McGrath, Terry; Haughey, Simon A.; Elliott, Christopher T.; Kelly, Simon; Suman, Michele; Rindy, T.; Taous, Fouad; García‐González, Diego L.; Singh, Dileep Kumar; Wai, Soe Nan; Zitek, Andreas; Jandrić, Zora; Almirall, José R.; Acosta, A.; Thao, C.; Islam, Marivil; Fung, L. Hoo; Kim, Jae‐Kyung; Burns, Malcolm; Strashnov, I.; Sarver, Ronald W.; Dua, A.K.; McVey, Claire; Shannon, Maeve; Galvin-King, Pamela; Logan, Natasha; Erasmus, Sara W.; Samokhin, A. S.; Bay, Lian Jie; Muhammad, Syahidah Akmal; Novák, V.; Pérez‐Marín, Dolores; Wunderlin, Daniel Alberto; Kasozi, Gabriel N.; Baeten, Vincent; Handagiripathira, H.M.N.L.

doi:10.1016/j.foodchem.2020.128718

Cited by 22 publications

(16 citation statements)

References 13 publications

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“…In terms of applications, NIR spectrometers can be divided into laboratory spectrometers, portable spectrometers and online spectrometers. In the past ten years, different types of NIR spectrometers have developed rapidly, such as visible/shortwave near-infrared spectrometers (Vis/SW-NIR) ( Barragan et al, 2021 ), miniaturized and handheld near-infrared spectrometers ( Mcgrath et al, 2020 ), near-infrared hyperspectral imaging (NIR-HSI), which integrates sample spectra and images ( Khamsopha et al, 2021 ).…”

Section: Principle Of Nir Spectroscopy and Chemometricsmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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Section: Principle Of Nir Spectroscopy and Chemometricsmentioning

confidence: 99%

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Zhang

Liu

Shen

et al. 2022

Current Research in Food Science

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“…have increased the pressure on food laboratories to investigate and explore fast screening methods for the detection of food adulteration. Spectroscopic methods are ideally suited for this purpose as they enable non-destructive, fast, green and reliable measurements [12][13][14][15][16][17][18]. These techniques are already applied in a wide variety of industry related areas including agriculture, cosmetics, pharmaceutical production, the textile industry and petroleum processing [19].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques are already applied in a wide variety of industry related areas including agriculture, cosmetics, pharmaceutical production, the textile industry and petroleum processing [19]. Recent developments in the direction of portable and miniaturized devices, in combination with appropriate software algorithms provided by manufacturers, have enabled the possibility for quality assessment by consumers to meet the demand for self-verifiable quality [14,19]. For this purpose, manufacturers provide a large number of miniaturized portable devices including: Thermo Fischer Scientific's microPHAZIR; Texas Instruments' NIRscan; Spectral Engines' NIRONE sensors; Si-Ware Systems' NeoSpectra scanner; SouthNest Technology's nanoFTIR NIR; and the devices used in this study namely: the VIAVI solutions MicroNIR, the Consumer Physics SCiO and the Tellspec Enterprise Sensor [15,16,20].…”

Section: Introductionmentioning

confidence: 99%

Portable vs. Benchtop NIR-Sensor Technology for Classification and Quality Evaluation of Black Truffle

et al. 2022

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Truffles represent the best known and most expensive edible mushroom. Known as Ascomycetes, they belong to the genus Tuber and live in symbiosis with plant host roots. Due to their extraordinary taste and smell, truffles are sold worldwide for high prices of up to 3000–5000 euros per kilogram (Tuber magnatum PICO). Amongst black truffles, the species Tuber melanosporum VITTAD. is highly regarded for its organoleptic properties. Nonetheless, numerous different sorts of black truffle are offered at lower prices, including Tuber aestivum VITTAD., Tuber indicum and Tuber uncinatum, which represent the most frequently consumed types. Because truffles do not differ visually for inexperienced consumers, food fraud is likely to occur. In particular, for the highly prized Tuber melanosporum, which morphologically forms very similar fruiting bodies to those of Tuber indicum, there is a risk of fraud via imported truffles from Asia. In this study, 126 truffle samples belonging to the four mentioned species were investigated by four different NIR instruments, including three miniaturized devices—the Tellspec Enterprise Sensor, the VIAVI solutions MicroNIR 1700 and the Consumer Physics SCiO—working on different technical principles. Three different types of measurement techniques were applied for all instruments (outer shell, rotational device and fruiting body) in order to identify the best results for classification and quality assurance in a non-destructive manner. Results provided differentiation with an accuracy up to 100% for the expensive Tuber melanosporum from Tuber indicum. Classification between Tuber melanosporum, Tuber indicum, Tuber aestivum and Tuber uncinatum could also be achieved with success of 100%. In addition, quality monitoring including discrimination between fresh and frozen/thawed, and prediction of the approximate date of harvesting, was performed. Furthermore, feasibility studies according to the geographical origin of the truffle were attempted. The presented work compares the performance for prediction and quality monitoring of portable vs. benchtop NIR devices and applied measurement techniques in order to be able to present a suitable, accurate, fast, non-destructive and reliable method for consumers.

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“…Other aspects which must be taken into account include the sensor's cost and ability to autonomously and non-invasively perform real-time measurements in production environments. Although many different sensing techniques exist, the most popular ones within food and drink manufacturing include visible imaging (Wu and Sun, 2013;Tomasevic et al, 2019), Near-Infrared (NIR) spectroscopy (Porep et al, 2015;McGrath et al, 2021), hyperspectral imaging (Huang et al, 2014;Saha and Manickavasagan, 2021), X-ray (Mathanker et al, 2013;de Medeiros et al, 2021), Ultrasonic (US) (Mathanker et al, 2013;Fariñas et al, 2021), microwave (Farina et al, 2019), and terahertz (Ok et al, 2014;Ren et al, 2019). The majority of previous work has focused on sensing the properties of the food materials but sensors have also been deployed to monitor processes such as mixing (Bowler et al, 2020a) and the fouling of heat exchangers (Wallhäußer et al, 2012).…”

Section: Online Sensorsmentioning

confidence: 99%

Intelligent Sensors for Sustainable Food and Drink Manufacturing

Watson

Bowler

Rady

et al. 2021

Front. Sustain. Food Syst.

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Food and drink is the largest manufacturing sector worldwide and has significant environmental impact in terms of resource use, emissions, and waste. However, food and drink manufacturers are restricted in addressing these issues due to the tight profit margins they operate within. The advances of two industrial digital technologies, sensors and machine learning, present manufacturers with affordable methods to collect and analyse manufacturing data and enable enhanced, evidence-based decision making. These technologies will enable manufacturers to reduce their environmental impact by making processes more flexible and efficient in terms of how they manage their resources. In this article, a methodology is proposed that combines online sensors and machine learning to provide a unified framework for the development of intelligent sensors that work to improve food and drink manufacturers' resource efficiency problems. The methodology is then applied to four food and drink manufacturing case studies to demonstrate its capabilities for a diverse range of applications within the sector. The case studies included the monitoring of mixing, cleaning and fermentation processes in addition to predicting key quality parameter of crops. For all case studies, the methodology was successfully applied and predictive models with accuracies ranging from 95 to 100% were achieved. The case studies also highlight challenges and considerations which still remain when applying the methodology, including efficient data acquisition and labelling, feature engineering, and model selection. This paper concludes by discussing the future work necessary around the topics of new online sensors, infrastructure, data acquisition and trust to enable the widespread adoption of intelligent sensors within the food and drink sector.

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The potential of handheld near infrared spectroscopy to detect food adulteration: Results of a global, multi-instrument inter-laboratory study

Cited by 22 publications

References 13 publications

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Application of near-infrared spectroscopy for the nondestructive analysis of wheat flour: A review

Portable vs. Benchtop NIR-Sensor Technology for Classification and Quality Evaluation of Black Truffle

Intelligent Sensors for Sustainable Food and Drink Manufacturing

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