Analysis of secondary biochemical components in maize flour samples by NIR (near infrared reflectance) spectroscopy

Kahrıman, Fatih; Onaç, İskender; Öner, Fatih; Mert, Figen; Egesel, Cem Ömer

doi:10.1007/s11694-020-00479-0

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…The protein content in these cereals is one of the key parameters determining nutritional grain values in human diets and its suitability for food industries [ 15 , 16 , 17 , 18 , 25 ]. Currently, NIRS-based methods are used for the rapid prediction of organic grain components [ 15 , 16 , 17 , 18 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 56 , 57 , 58 , 59 , 63 , 64 , 65 , 66 , 89 , 90 , 92 , 93 , 94 ]. However, there are not many examples for minor cereal grains (such as millets in the presented study [ 89 , 90 , 123 ]).…”

Section: Discussionmentioning

confidence: 99%

“…For the predictions of organic grain composition from NIR spectral reflectance, deterministic methods have been widely used (e.g., MLR, PCR, and PLS regression [ 15 , 16 , 17 , 18 , 19 , 20 , 25 ]). These methods were mostly specific to a single species [ 26 , 27 , 28 , 29 , 30 , 31 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. The adoption of ML methods in NIR spectroscopy research has quite recently begun, together with the increasing availability of computing power and efficient learning algorithms [ 34 , 37 , 118 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the case of the cereal-based food and feed industry [ 25 , 42 , 43 ], benchtop NIRS systems with calibrations based on single-cereal species are routinely utilized—rice [ 26 , 28 , 30 , 32 , 36 , 40 , 44 , 45 , 46 , 47 , 48 ], sorghum [ 43 , 49 , 50 ], wheat [ 27 , 51 , 52 , 53 , 54 , 55 ], corn [ 31 , 53 , 56 , 57 , 58 , 59 , 60 ], and barley [ 53 , 61 , 62 , 63 ]. These typically use a large number of single-species samples from different environments representing a relevant range of target trait variability [ 3 , 52 , 64 , 65 , 66 , 67 , 68 , 69 ].…”

Section: Introductionmentioning

confidence: 99%

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NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals

Chadalavada

Anbazhagan

Ndour

et al. 2022

Sensors

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Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

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