Determination of carotenoid and tocopherol content in maize flour and oil samples using near-infrared spectroscopy

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

doi:10.1080/00387010.2019.1671872

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…We also detected a high correlation between tocopherol and carotenoid contents. This finding agrees with the results of Kahriman et al (2019). The estimated broad-sense heritability for oil content (hBS = 63.9%) is within the limits specified in the literature.…”

Section: Discussionsupporting

confidence: 91%

Screening Turkish maize landraces for kernel oil content and oil quality traits

Kahrıman

Aktaş

Songur

et al. 2020

Plant Genet. Resour.

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In this study, maize landraces, previously collected from different regions of Turkey through a national maize breeding project, were screened for kernel oil content and oil quality. After a seed reproduction in 2017, the screening trial was conducted in 2018, using an Augmented Experimental Design with 192 local maize populations along with seven check hybrids. Data were collected on oil, oleic acid, linoleic acid, monounsaturated (MUFA), polyunsaturated and saturated fatty acids, total carotenoid and total tocopherol contents. The results showed that there was a considerable amount of genetic variation among the Turkish maize landraces for almost all the traits investigated in this study. Some of the evaluated landraces are available that could be considered as high oil variety or special genotypes in terms of certain oil quality traits. Landraces had significantly higher values than checks for oil content, oleic acid, MUFA and tocopherol contents. Genetic analyses suggest that the kernel quality traits could be successfully manipulated using the investigated plant material. Heritability values were found high for all the traits of interest, except for saturated fatty acid and total carotenoid content. The promising landraces detected in this study could be exploited in future breeding programmes upon further evaluations at the population level.

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Section: Discussionsupporting

confidence: 91%

Screening Turkish maize landraces for kernel oil content and oil quality traits

Kahrıman

Aktaş

Songur

et al. 2020

Plant Genet. Resour.

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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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“…Some of the major attributes accessed using IR spectroscopy include phenolic content, carotenoid content, peroxide value, refractive index, yellowness index, and fatty acids composition. These have been carried out for different oil products like olive oil ( Inarejos-García et al, 2013 ; Cayuela and García, 2017 ), palm oil ( Mba et al, 2014 ), maize oil ( Kahrıman et al, 2019 ) and vegetable oil ( Pereira et al, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy

et al. 2022

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The pomegranate kernel oil has gained global awareness due to the health benefits associated with its consumption; these benefits have been attributed to its unique fatty acid composition. For quality control of edible fats and oils, various analytical and calorimetric methods are often used, however, these methods are expensive, labor-intensive, and often require specialized sample preparation making them impractical on a commercial scale. Therefore, objective, rapid, accurate, and cost-effective methods are required. In this study, Fourier transformed near-infrared (FT-NIR) and mid-infrared (FT-MIR) spectroscopy as a fast non-destructive technique was investigated and compared to qualitatively and quantitatively predict the quality attributes of pomegranate kernel oil (cv. Wonderful, Acco, Herskawitz). For qualitative analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) was applied. Based on OPLS-DA, FT-MIR spectroscopy resulted in 100% discrimination between oil samples extracted from different cultivars. For quantitative analysis, partial least squares regression was used for model development over the NIR region of 7,498–940 and 6,102–5,774 cm−1 and provided the best prediction statistics for total carotenoid content (R2, coefficient of determination; RMSEP, root mean square error of prediction; RPD, residual prediction deviation; R2 = 0.843, RMSEP = 0.019 g β-carotene/kg, RPD = 2.28). In the MIR region of 3,996–1,118 cm−1, models developed using FT-MIR spectroscopy gave the best prediction statistics for peroxide value (R2 = 0.919, RMSEP = 1.05 meq, RPD = 3.54) and refractive index (R2 = 0.912, RMSEP = 0.0002, RPD = 3.43). These results demonstrate the potential of infrared spectroscopy combined with chemometric analysis for rapid screening of pomegranate oil quality attributes.

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Determination of carotenoid and tocopherol content in maize flour and oil samples using near-infrared spectroscopy

Cited by 14 publications

References 14 publications

Screening Turkish maize landraces for kernel oil content and oil quality traits

Screening Turkish maize landraces for kernel oil content and oil quality traits

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

Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil by Fourier Transform Near-Infrared and Mid-Infrared Spectroscopy

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