NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

Caramês, Elem Tamirys dos Santos; Piacentini, Karim C.; Alves, Lucas Teixeira; Rocha, Liliana O.

doi:10.1080/19440049.2020.1778189

Cited by 19 publications

(16 citation statements)

References 36 publications

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“…In order to better explain the potentialities of our method, we report some recent studies published in the last three years showing the great practical-applicative potentialities of the use of NIR in monitoring mycotoxin contamination in several matrices used in animal or human nutrition in Table 8 . In particular, the results obtained in our study were in line with the classification studies reported below [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. The study reported here gave a practical application of NIR calibration in order to underline the importance of this type of approach as a practical tool to assess the safety of silage.…”

Section: Discussionsupporting

confidence: 92%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

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Section: Discussionsupporting

confidence: 92%

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Ghilardelli

Barbato

Gallo

2022

Toxins

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“…DON, known also as vomitoxin, is one of the most abundant mycotoxins in the world, produced by the genus Fusarium [29]. It causes mainly alimentary disorders, immune function depression, and gastroenteritis [30,31]. The T-2 toxin is known for its anorexigenic effects on the gastrointestinal tract, as well as disruption of the nervous system of animals [26,29].…”

Section: Introductionmentioning

confidence: 99%

“…The T-2 toxin is known for its anorexigenic effects on the gastrointestinal tract, as well as disruption of the nervous system of animals [26,29]. Aflatoxins are mycotoxins produced by Aspergillus flavus and A. parasiticus, which cause acute toxicity in low concentrations, liver necrosis, cancer, and in some cases, the death of humans and animals [30][31][32]. It is considered one of the most toxic mycotoxins [33,34].…”

Section: Introductionmentioning

confidence: 99%

Does Digestate Dose Affect Fodder Security and Nutritive Value?

et al. 2022

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With the rising interest in digestate use as a fertilizer on permanent cultures, there is a need to examine its effects on food and feed quality. This study is focused on the use of digestate in grassland fertilization and its effects on nutritive value parameters such as mycotoxin contamination (deoxynivalenol, aflatoxin, and T-2 toxin) and nutrient content (crude protein, crude fat, crude fiber, ash, nitrogen-free extract, digestibility of organic matter, acid detergent fiber, and ash-free neutral detergent fiber). The experiment was carried out in the Czech Republic, and the effects of fertilization regime, year, and harvest date (summer and fall cuts) on nutritive value were observed. An effect of the year on DON, AFB1, and T-2 contamination levels was observed. An effect of the harvest or fertilization regime on mycotoxin contamination was not observed. Significant differences were observed in the content of all nutrients, except ash, depending on the year. Differences were found only in the case of ADF levels, depending on the harvest date, as well; however, no differences were found between fertilization regimes. Our findings suggest that digestate does not negatively affect fodder in terms of nutritive value nor safety.

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“…As one of the green and non-destructive testing methods, near-infrared spectroscopy has been used to analyze qualitative and quantitative data by constructing multivariate correction models. At present, it has been widely used in petrochemical, pharmaceutical, agriculture, forestry, and other fields [1][2][3][4]. However, the basic characteristics of the tested sample, the differences in the structure and principles between spectrometers, and the variations in the detected environment all have influenced on the reliability of the model, and can cause signal drift and absorption peak shape changes in the results of near-infrared detection, resulting in the disturbed accuracy of the model [5].…”

Section: Introductionmentioning

confidence: 99%

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

Zhang

et al. 2022

Sensors

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For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

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NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley

Cited by 19 publications

References 36 publications

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Does Digestate Dose Affect Fodder Security and Nutritive Value?

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

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