Lignin accumulates in the cell walls of specialized cell types to enable plants to stand upright and conduct water and minerals, withstand abiotic stresses, and defend themselves against pathogens. These functions depend on specific lignin concentrations and subunit composition in different cell types and cell wall layers. However, the mechanisms controlling the accumulation of specific lignin subunits, such as coniferaldehyde, during the development of these different cell types are still poorly understood. We herein validated the Wiesner test (phloroglucinol/HCl) for the restrictive quantitative in situ analysis of coniferaldehyde incorporation in lignin. Using this optimized tool, we investigated the genetic control of coniferaldehyde incorporation in the different cell types of genetically-engineered herbaceous and woody plants with modified lignin content and/or composition. Our results demonstrate that the incorporation of coniferaldehyde in lignified cells is controlled by (a) autonomous biosynthetic routes for each cell type, combined with (b) distinct cell-to-cell cooperation between specific cell types, and (c) cell wall layer-specific accumulation capacity. This process tightly regulates coniferaldehyde residue accumulation in specific cell types to adapt their property and/or function to developmental and/or environmental changes.

Tropical root and tuber crops (cassava, sweet potato, taro, and yam) are staples in developing countries where rapid urbanization is strengthening the demand for flour based foods. Quality control techniques are still under development, and when available, laboratory analyses are too expensive. The objectives of this study were to calibrate Near-infrared spectroscopy (NIRS) for routine analysis of flours and to test its reliability to determine their major constituents. Flours prepared from 472 accessions (traditional varieties and breeding lines) were analyzed for their starch, total sugars, cellulose, total nitrogen, and ash (total minerals) contents. The near-infrared (350-2500 nm) spectra of all samples were measured. Calibration equations with cross and independent validation for all analytical characteristics were computed using the partial least squares method. Models were developed separately for each of the four crop species and by combining data from all spp. to predict values within each of them. The quality of prediction was evaluated on a test set of 94 accessions (20%) by standard error of prediction (SEP) and r2 parameters between the measured and the predicted values from cross-validation. Starch, sugar, and total nitrogen content could be predicted, respectively, with 87%, 86%, and 93% confidence, whereas ash (minerals) could be predicted with 71%, and cellulose was not predictable (r2=0.31). The statistical parameters obtained for starch, sugars, and total nitrogen are of special interest for flour quality control. These constituents are quantitatively the most important in the chemical composition of flours, and starch content is negatively correlated with sugars and total nitrogen. NIRS is a low cost technique well adapted to the conditions in developing countries and can be used for the high-throughput screening of a great number of samples. Possible applications are discussed.

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