Predicting food nutrition facts using pocket-size near-infrared sensor

Thong, Yoke Jia; Nguyen, Thuong; Zhang, Qing; Karunanithi, Mohan; Lei, Yu

doi:10.1109/embc.2017.8036931

Cited by 20 publications

(16 citation statements)

References 10 publications

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“…To our knowledge, there is very limited information concerning the use of miniaturized “pocket” infrared tools in the pet food sector, and the present paper is the first attempt to predict the composition of dry pet food using the miniaturized NIR scanner SCiO™ (Consumer Physics Inc., Tel Aviv, Israel). Moreover, other researchers have reported the potential of this instrument for the prediction of energy and carbohydrate contents in drinks [ 19 ], total soluble solids, maturity in fruits [ 20 ], meat composition [ 21 ], intact casein, and total protein in cheese [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Goi

Simoni

Righi

et al. 2020

Animals

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The aim of the present study was to investigate the ability of a handheld near-infrared spectrometer to predict total and gelatinized starch, insoluble fibrous fractions, and mineral content in extruded dry dog food. Intact and ground samples were compared to determine if the homogenization could improve the prediction performance of the instrument. Reference analyses were performed on 81 samples for starch and 99 for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), and minerals, and reflectance infrared spectra (740 to 1070 nm) were recorded with a SCiO™ near-infrared (NIR) spectrometer. Prediction models were developed using modified partial least squares regression and both internal (leave-one-out cross-validation) and external validation. The best prediction models in cross-validation using ground samples were obtained for gelatinized starch (residual predictive deviation, RPD = 2.54) and total starch (RPD = 2.33), and S (RPD = 1.92), while the best using intact samples were obtained for gelatinized starch (RPD = 2.45), total starch (RPD = 2.08), and K (RPD = 1.98). Through external validation, the best statistics were obtained for gelatinized starch, with an RPD of 2.55 and 2.03 in ground and intact samples, respectively. Overall, there was no difference in prediction models accuracy using ground or intact samples. In conclusion, the miniaturized NIR instrument offers the potential for screening purposes only for total and gelatinized starch, S, and K, whereas the results do not support its applicability for the other traits.

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Section: Introductionmentioning

confidence: 99%

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Goi

Simoni

Righi

et al. 2020

Animals

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“…From a Internet of Things (IoT) perspective, projects are being developed on measuring food nutrition facts, through a pocket-size non-intrusive near-infrared (NIR) scanner [37] or [38]. With this device are recorded NIR spectra reflected from foods and use them as features to predict nutrients, for example energy and carbohydrate.…”

Section: Iotmentioning

confidence: 99%

“…In respect of the "unsupervised" feature, in many cases, the system needs assi 283 order to run it, either by for inserting the data or for starting the own system. In respect of the "unsupervised" feature, in many cases, the sys 283 order to run it, either by for inserting the data or for starting the ow 284 IoT [37] Version October 12, 2018 submitted to Entropy 8 of 9…”

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confidence: 99%

Monitoring Food Intake in an Aging Population: A Survey on Technological Solutions

et al. 2018

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The aging of the population has increased the research efforts focused on eldercare. In this area, nutrition is a topic of particular interest. A significant number of problems related with aging have their origin in a deficient nutrition. Many elders suffer changes in their nutritional patterns and these changes can cause serious deterioration of their physical condition. Therefore, to be able to address nutritional problems in the elderly, their nutritional patterns must be recorded and analyzed in an easy and efficient way. From a technological point of view, numerous works focus on monitoring the food intake, not only of elders but also of the general population. However, these works usually do not take into account the problems associated with an aging population. In this paper we present a survey of existing technological solutions for monitoring food intake. The analyzed solution are categorized based on their technological implementation and their adaptation to the needs of an aging population.

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“…Newer alternatives for determining dietary intake include audio signal processing, inertial sensing, image processing, non-intrusive near-infrared scanning, and gesture recognition interfacing [ 21 - 24 ]. Some authors maintain that more research is needed to develop these and other tools that are more objective and precise and that resources should be invested to this end [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records

Béjar¹,

Reyes²,

García-Perea³

2018

JMIR Mhealth Uhealth

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BackgroundOne of the greatest challenges in nutritional epidemiology is improving upon traditional self-reporting methods for the assessment of habitual dietary intake.ObjectiveThe aim of this study was to evaluate the relative validity of a new method known as the current-day dietary recall (or current-day recall), based on a smartphone app called 12-hour dietary recall, for determining the habitual intake of a series of key food and drink groups using a food frequency questionnaire (FFQ) and four dietary records as reference methods.MethodsUniversity students over the age of 18 years recorded their consumption of certain groups of food and drink using 12-hour dietary recall for 28 consecutive days. During this 28-day period, they also completed four dietary records on randomly selected days. Once the monitoring period was over, subjects then completed an FFQ. The two methods were compared using the Spearman correlation coefficient (SCC), a cross-classification analysis, and weighted kappa.ResultsA total of 87 participants completed the study (64% women, 56/87; 36% men, 31/87).For e-12HR versus FFQ, for all food and drink groups, the average SCC was 0.70. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 51.5%; exact agreement + adjacent was 91.8%, and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.51.For e-12HR versus the four dietary records, for all food and drink groups, the average SCC was 0.63. Cross-classification analysis revealed that the average percentage of individuals classified in the exact agreement category was 47.1%; exact agreement + adjacent was 89.2%; and no participant (0%) was classified in the extreme disagreement category. The average weighted kappa was 0.47.ConclusionsCurrent-day recall, based on the 12-hour dietary recall app, was found to be in good agreement with the two reference methods (FFQ & four dietary records), demonstrating its potential usefulness for categorizing individuals according to their habitual dietary intake of certain food and drink groups.

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Predicting food nutrition facts using pocket-size near-infrared sensor

Cited by 20 publications

References 10 publications

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Application of a Handheld Near-Infrared Spectrometer to Predict Gelatinized Starch, Fiber Fractions, and Mineral Content of Ground and Intact Extruded Dry Dog Food

Monitoring Food Intake in an Aging Population: A Survey on Technological Solutions

Electronic 12-Hour Dietary Recall (e-12HR): Comparison of a Mobile Phone App for Dietary Intake Assessment With a Food Frequency Questionnaire and Four Dietary Records

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