Reconsidering the Need for Empirical Alignment and Wavelength Calibration Steps in the Building of a Dispersive NIR Spectrometer with an Application for Ethanol Quantification Using a Polymer Filament 3D Printer

The construction of a dispersive optical spectrometer using three-dimensional (3D) design software and printing, without applying any optical adjustments, its validation, and application to quantification of ethanol in multiproduct liquids, is the objective of this work. A 3D design software was used to design a near-infrared (NIR) spectrometer in the region from 800 to 1600 nm from the dimensions of commercially available optical components. The project was printed on a polymer filament 3D printer, and the components were fitted to the printed part. Software calculations using the model design parameters were applied to attribute the wavelength values to the abscissa axis in the spectra and estimate errors due to 3D printing limitations. The alignment of the spectrum was proven using the chloroform absorbance spectrum, which presented a maximum mispositioning of 4.1 nm concerning the literature data and effective bandwidths equivalent to commercial instruments. The 3D-printed instrument was applied to quantify ethanol in samples of cachaça, rum, beer, brandy, whiskey, vodka, mouth wash, alcohol gel, and commercial alcohol solutions. Partial least-squares regression models were built for the 3D-printed instrument and two commercial NIR instruments, the MPA II (Bruker) and the NIR DLP NIRscan (Texas Instruments), using a group of 180 standards. The three instruments reached excellent predictive capability with similar root mean square error of cross-validation (2.36-2.68) and prediction (2.31-2.87). The correlation coefficient of cross-validation and prediction for all models were between 0.97 and 0.98. The results show the feasibility of building a 3D-printed dispersive spectrometer ready for application with the simple docking of the optics, presenting acceptable accuracy to the project design concerning the printing limitations.

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

Assessing resistance of sugarcane varieties to sugarcane borer Diatraea saccharalis Fab. (Lepidoptera: Crambidae)

In this study, we investigated resistance traits to the sugarcane borer Diatraea saccharalis Fab. (Lepidoptera: Crambidae) in the leaves and stalks of six sugarcane cultivars in a series of greenhouse and laboratory assays. Investigation of plant factors and infestation rates to better discriminate stalk damage by the sugarcane borer indicated that infestation of 7-month-old, single plants with 20 larvae at the third or fourth instar per plant was suitable to assess tunneling length. Three cultivars (i.e. SP803280, RB928064, and RB835486) had lower stalk damage (i.e. tunnel length) than cultivar SP891115, which exhibited relatively greater susceptibility to tunneling by the borer. The time required for the larvae to enter the sugarcane stalk was longer for cultivar SP803280, indicating resistance traits on the stalk surface, which correlated with lower stalk damage. Larvae feeding on SP813250 stalks had the lowest weight gain, indicating that this cultivar has resistance traits to larval development within its stalks. Cultivars RB867515 and SP891115 resulted in the highest mortality of early-stage larvae feeding on leaves, indicating the presence of resistance factors in their leaves. Multi-trait cluster and principal component analyses placed the cultivars into three and four clusters, respectively. The cultivars placed in different groups that exhibited resistance to leaf feeding, stalk entrance, and tunneling by the sugarcane borer could be used for crossings in sugarcane breeding programs with the goal of obtaining higher levels of resistance to D. saccharalis.

Selecting sugarcane genotypes by the selection index reveals high gain for technological quality traits

Sugarcane (Saccharum sp) is one of the most promising crops and researchers have sought for renewable alternative energy sources to reduce CO₂ emission. The study of strategies, which allow breeders in the selection of superior genotypes for many traits simultaneously, is important. Therefore, the objectives of this study were: i) to apply path analysis to better understand the relationship between the lignocellulosic traits and technological quality traits with total recoverable sugars (TRS) and ii) to use several multivariate selection indexes to predict the genetic gain and to select superior genotypes in the sugarcane breeding. A total of 40 sugarcane genotypes were evaluated in an experimental design using incomplete blocks with two replicates. The follow traits were evaluated: dry matter (DM), total soluble solids (BRIX), apparent sucrose content in the juice (POL), apparent sucrose content in sugarcane (POLS), fiber content (FIB), purity (PUR), TRS, lignin content (LC), cellulose content (CC), hemicellulose content (HC), and ash content (AC). These traits were analyzed by analysis of variance, phenotypic correlation network, path analysis, and selection index. The highest direct effect on TRS was obtained by POLS (0.337), POL (0.299), BRIX (0.227), and FIB (-0.146). The estimates of phenotypic correlation between these characters and TRS were in the same direction, which demonstrated a cause-and-effect relationship. The highest indirect effect was of POL via POLS (0.331) followed by POLS via POL (0.294). BRIX presented high indirect effects via POLS (0.266) and via POL (0.246). On the other hand, FIB presented negative indirect effects via POLS (-0.169) and POL (-0.103). In conclusion, path analysis and index selection are useful strategies to help breeders in the selection of superior genotypes in sugarcane.

Selection index using the graphical area applied to sugarcane breeding

This study aimed to develop a multivariate selection index based on the graphical area of a polygon formed by standardized values, also known as radar chart. This methodology may be used to assist selection of superior genotypes in sugarcane breeding programs. Seven technological traits in 37 sugarcane genotypes were evaluated. An area index (AI) was constructed and the resulting polygon areas were used to rank genotypes under selection. In this study, we propose the use of restricted maximum likelihood to estimate genetic parameters and mixed model equations to predict genotypic and breeding values. The area of each polygon was calculated for phenotypic, genotypic, and estimated breeding values. Thereby, the genotypes with larger area can be selected based on a detailed a posteriori evaluation of the radar charts. The proposed AI can be adjusted based on the breeders' specific interests, it is perfectly useful in other crops, and may also be applied to studies on genotype-environment interactions. Moreover, AI is a powerful tool that can evaluate trait stability of genotypes based on slight differences in the area formed by each genotype. Hence, this method is easy to apply and shows great potential for use in sugarcane breeding programs as well as in other breeding programs.

Co-expression network analysis reveals transcription factors associated to cell wall biosynthesis in sugarcane

Sugarcane is a hybrid of Saccharum officinarum and Saccharum spontaneum, with minor contributions from other species in Saccharum and other genera. Understanding the molecular basis of cell wall metabolism in sugarcane may allow for rational changes in fiber quality and content when designing new energy crops. This work describes a comparative expression profiling of sugarcane ancestral genotypes: S. officinarum, S. spontaneum and S. robustum and a commercial hybrid: RB867515, linking gene expression to phenotypes to identify genes for sugarcane improvement. Oligoarray experiments of leaves, immature and intermediate internodes, detected 12,621 sense and 995 antisense transcripts. Amino acid metabolism was particularly evident among pathways showing natural antisense transcripts expression. For all tissues sampled, expression analysis revealed 831, 674 and 648 differentially expressed genes in S. officinarum, S. robustum and S. spontaneum, respectively, using RB867515 as reference. Expression of sugar transporters might explain sucrose differences among genotypes, but an unexpected differential expression of histones were also identified between high and low Brix° genotypes. Lignin biosynthetic genes and bioenergetics-related genes were up-regulated in the high lignin genotype, suggesting that these genes are important for S. spontaneum to allocate carbon to lignin, while S. officinarum allocates it to sucrose storage. Co-expression network analysis identified 18 transcription factors possibly related to cell wall biosynthesis while in silico analysis detected cis-elements involved in cell wall biosynthesis in their promoters. Our results provide information to elucidate regulatory networks underlying traits of interest that will allow the improvement of sugarcane for biofuel and chemicals production.

Selection between and within full-sib sugarcane families using the modified BLUPIS method (BLUPISM)

The objective of this study was to assess the efficiency of a modification of the simulated individual best linear unbiased prediction (BLUPIS) procedure, which is used for the approximation of classic individuals (BLUPI) for selection between and within sugarcane families. A total of 110 full-sib families were employed in an experiment initiated in 2007 using a randomized block design with five replicates. The variable tons of stalks per hectare was measured from a plot containing 20 plants. The modified BLUPIS (BLUPISM) procedure showed a 0.98 correlation with BLUPI, thus demonstrating great efficiency in selecting individuals in sugarcane families during the initial phase of genetic breeding programs.

Selfing confirmation in sugarcane by using simple sequence repeat markers: an individual reciprocal recurrent selection scheme

Superior inbred clones selected in S1 families can integrate an individual reciprocal recurrent selection program in sugarcane by eliminating the genetic load of the population and exploring superior hybrid combinations. Molecular markers can be used for reliable identification of the true selfing-derived clones in these S1 populations. The objective of this study was to confirm true self-fertilized individuals in sugarcane families using microsatellite markers aimed at the use of self-fertilized plants in an individual reciprocal recurrent selection strategy. Self-fertilized individuals from five cultivars were genotyped with eight simple sequence repeat (SSR) markers. The markers generated 62 polymorphic markers, with an average of seven polymorphic alleles across the cultivars tested. Three loci revealed highly informative bands and were used to assess the level of selfing in five S1 families. Selfing in these families ranged from 71.7 to 97.6%. The SSR loci provide a reliable and accurate method to identify S1 progenies in sugarcane crosses and can be used as a tool to assist selection strategies in sugarcane breeding programs.

Genetic diversity and coefficient of parentage between clones and sugarcane varieties in Brazil

The success of the development of new sugarcane varieties is associated with the ability to correctly select the genitor. The aim of this study was to evaluate the genetic diversity between 113 clones and sugarcane varieties using the Ward-modified location model procedure with added information about the coefficient of parentage and endogamy. In this study, data was used from 100 experiments that evaluated clones; the experimental phase was conducted in 70 places between the years 2002 and 2009 on the outlining in random blocks. According to the diversity analysis, 3 groups formed: G1, G2, and G3, which were composed of 58, 8, and 47 genotypes, respectively. The clones of groups G1 and G3 were the most outstanding. Thus, biparental crossbreeding involving clones and varieties of these 2 groups can efficiently obtain transgressive genotypes. Knowledge of the heterotypic groups indicated by the Ward-modified location model method, along with the parentage information, will make it a lot easier to define the desirable and undesirable crossbreeds for public and private breeding programs that develop sugarcane varieties.

Selection of sugar cane families by using BLUP and multi-diverse analyses for planting in the Brazilian savannah

This study evaluated different strategies to select sugar cane families and obtain clones adapted to the conditions of the Brazilian savannah. Specifically, 7 experiments were conducted, with 10 full sib families, and 2 witnesses in common to all experiments, in each experiment. The plants were grown in random blocks, with witnesses in common (incomplete blocks), and 6 repetitions of each experiment. The data were analyzed through the methodology of mixed patterns, in which the matrices of kinship between the families were identified by the method of restricted maximum likelihood. The characteristics that were evaluated included soluble solids content (BRIX), BRIX ton/ha, average mass of a culm, number of culms/m, and tons of culms/ha. A multi-diverse alternative based on the analysis of groupings by using the UPGMA method was used to identify the most viable families for selection, when considering the genotypic effects on all characteristics. This method appeared suitable for the selection of families, with 5 family groups being formed. The families that formed Group 2 appeared superior to all other families for all the evaluated characteristics. It is recommended that the families in Group 2 are preferentially used in sugar cane improvement programs to obtain varieties optimally adapted to the conditions of the Brazilian savannah.