Reflectance colorimetry as a simple method for estimating carotenoid content in maize grain

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction

Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.

Induced changes of phenolic compounds in turmeric bread by UV-C radiation

Phenolic compounds of breads added with turmeric at different concentrations (A: 0, B: 1.25, C: 2.5, D: 5 and E:10%) and radiated by UV-C (I. 0, II. 15, III. 30 and IV. 60 s), have been evaluated by HPLC (High-performance liquid chromatography). It is shown that: (i) UV-C radiation modifies the content of phenolic compounds as a function of the percentage of addition of turmeric and the exposure time. There were significant differences (ρ ≤ 0.05) in the concentration of phenolic acids of the turmeric bread (TB): 0 s (sinapic, chlorogenic, protocatechuic), 15 s (chlorogenic, ferulic, protocatechuic, p-hydroxybenzoic, gallic), 30 s (chlorogenic and gallic) and 60 s (chlorogenic). (ii) In TB without radiation appeared, the sinapic, beta resorcylic, syringic and ferulic acids. In the radiation of bread at 15 s, the phenolic acids chlorogenic, ferulic, protocatechuic, p-hydroxybenzoic, gallic, had the highest concentration in the breads added with turmeric at 10% (0.02 μg mL⁻¹), 10% (0.38 μg mL⁻¹), 1.25, 2.5, 5% (0.39 μg mL⁻¹), 10% (1.06 μg mL ⁻¹) and 0% (1.10 μg mL⁻¹). (iii) There was a degradation of phenolic acids due to UV-C radiation at 30 and 60 s. At 15 s radiation, sinapic, beta resorcylic, syringic and ferulic acids were not detected in turmeric breads from breads added with turmeric at (1.25, 1.25, 0 and 0%). In radiation at 60 s, beta resorcylic, syringic and ferulic acids were not detected in any bread added with turmeric. In addition, measurements of proximate chemistry, color, sensory analysis, and number of fungal colonies were performed. It is important to mention that the sanitary quality is improved by both UV-C radiation and turmeric. However, the highest results in sanitary quality improvement were due to turmeric.

Vitamin B2 concentration in cow milk: Quantification by a new UHPLC method and prediction by visible and near-infrared spectral analysis

Until now, there are few information on vitamin B₂ concentration variability in milk. In this study, a novel analytical method to quantify total vitamin B₂ in milk was developed and applied on 676 samples. In parallel, spectral analysis (colorimetry and near infrared spectroscopy) were performed to develop prediction models of vitamin B₂ concentration in milk. The analytical method includes an acid and enzymatic extraction followed by vitamin B₂ quantification by Ultra High Performance Liquid Chromatography coupled with fluorimetry. Samples analysis showed a wide range of concentration from 0.78 to 4.58 mg/L with a mean of 2.09 ± 0.48 mg/L. Two prediction models based on colorimetric analysis allow estimation of vitamin B₂ concentration in milk. Thus, this work shows an analytical method and, for the first time, a prediction method to enable enhancement of researches on vitamin B₂ content of milk and its variation factors.

UV-Vis and CIELAB Based Chemometric Characterization of Manihot esculenta Carotenoid Contents

Vitamin A deficiency is a prevalent health problem in many areas of the world, where cassava genotypes with high pro-vitamin A content have been identified as a strategy to address this issue. In this study, we found a positive correlation between the color of the root pulp and the total carotenoid contents and, importantly, showed how CIELAB color measurements can be used as a non-destructive and fast technique to quantify the amount of carotenoids in cassava root samples, as opposed to traditional methods. We trained several machine learning models using UV-visible spectrophotometry data, CIELAB data and a low-level data fusion of the two. Best performance models were obtained for the total carotenoids contents calculated using the UV-visible dataset as input, with R² values above 90 %. Using CIELAB and fusion data, values around 60 % and above 90 % were found. Importantly, these results demonstrated how data fusion can lead to a better model performance for prediction when comparing to the use of a single data source. Considering all these findings, the use of colorimetric data associated with UV-visible and HPLC data through statistical and machine learning methods is a reliable way of predicting the content of total carotenoids in cassava root samples.

Long-term on-farm participatory maize breeding by stratified mass selection retains molecular diversity while improving agronomic performance

Modern maize breeding programs gave rise to genetically uniform varieties that can affect maize's capacity to cope with increasing climate unpredictability. Maize populations, genetically more heterogeneous, can evolve and better adapt to a broader range of edaphic–climatic conditions. These populations usually suffer from low yields; it is therefore desirable to improve their agronomic performance while maintaining their valuable diversity levels. With this objective, a long‐term participatory breeding/on‐farm conservation program was established in Portugal. In this program, maize populations were subject to stratified mass selection. This work aimed to estimate the effect of on‐farm stratified mass selection on the agronomic performance, quality, and molecular diversity of two historical maize populations. Multilocation field trials, comparing the initial populations with the derived selection cycles, showed that this selection methodology led to agronomic improvement for one of the populations. The molecular diversity analysis, using microsatellites, revealed that overall genetic diversity in both populations was maintained throughout selection. The comparison of quality parameters between the initial populations and the derived selection cycles was made using kernel from a common‐garden experiment. This analysis showed that the majority of the quality traits evaluated progressed erratically over time. In conclusion, this breeding approach, through simple and low‐cost methodologies, proved to be an alternative strategy for genetic resources’ on‐farm conservation.

Provitamin A potential of landrace orange maize variety (Zea mays L.) grown in different geographical locations of central Malawi

The provitamin A potential of landrace orange maize from different locations (A, B, C and D) of central Malawi has been evaluated. Physicochemical compositions, color, total carotenoid content (TCC), carotenoid profiles, and oxygen radical absorbance capacity (ORAC) and 2,2-diphenyl-1-picryhydrazyl (DPPH) free radical scavenging activity as antioxidant capacities of maize were determined. Color values of orange maize had correlations with β-cryptoxanthin (r>0.36). TCC of white and orange maize averaged 2.12 and 59.5 mg/kg, respectively. Lutein was the most abundant carotenoid (47.8%) in orange maize, followed by zeaxanthin (24.2%), β-carotene (16.4%) and β-cryptoxanthin (11.6%). Location D showed the highest levels of lutein, zeaxanthin and antioxidant capacity. Provitamin A content of orange maize met the target level (15 μg/g) of biofortification. Retinol activity equivalent (RAE) from β-cryptoxanthin and β-carotene in orange maize averaged 81.73 μg/100g. In conclusion, orange maize has the potential to be a natural source of provitamin A.