Near infrared spectroscopy for predicting quality indices in the organic fertiliser industry

The carbon to nitrogen (C/N) ratio can provide information on the capacity of an organic input to be transformed into humus [1 - 3], but it is not sufficient in some cases [4]. The lignin/nitrogen (Lig/N) ratio is another parameter used when modelling the transformation of organic materials [5]. An alternative estimate of degradability is the percentage of organic matter which is potentially resistant to mineralization over a long period of time. This estimate of the potentially humified organic matter (PHOM), calculated from an established equation based on the chemical composition, is normalised [7]. Quality indicators to determine the total carbon (C), nitrogen (N) and lignin content are expensive and time-consuming. In this study we attempted to predict the PHOM index, the lignin/N ratio and the C/N ratio directly by near infrared (NIR) spectroscopy as a measure of the potential degradability of organic waste. (Résumé d'auteur

Mise en oeuvre de la SPIR pour les productions animales dans les pays du Sud

Les instituts de recherche en productions animales des pays du Sud disposent parfois de laboratoires d'analyse. Cependant ces laboratoires rencontrent souvent des difficultés de fonctionnement soit techniques (défaut d'approvisionnement en eau ou électricité) soit humaines (difficultés à avoir et/ou conserver des agents formés). De plus, les centres de recherche disposent parfois de capacités d'investissement en équipements scientifiques assez bonnes (projets, coopération, dons) mais ne disposent pas des ressources financières pour les maintenir et les faire fonctionner correctement. La production de données de référence fiables est donc généralement très difficile et aléatoire. Dans ce contexte où il est également de plus en plus difficile de faire voyager des échantillons pour des raisons sanitaires ou réglementaires, l'utilisation de la SPIR est une solution efficace pour permettre la production de données expérimentales à grande échelle. L'UMR Selmet travaille à la mise en place d'un réseau SPIR en Afrique et Océan Indien. Cette stratégie vise à appuyer nos partenaires d'une part à s'équiper en SPIR pour leur permettre d'acquérir une capacité de caractérisation de leurs ressources alimentaires et d'autre part à les accompagner dans le développement de cette technique. Pour l'UMR Selmet l'accompagnement des partenaires passe par la formation et par le développement et le déploiement d'outil d'aide à la prédiction (plateforme de prédiction en ligne " NIRSPred "). Il est cependant essentiel d'associer les partenaires à la production de données de référence et d'éviter de les rendre totalement dépendants de bases centralisées. L'aide au suivi de la qualité des spectromètres (création automatisée de carte de contrôle " ASDMonitor ") est également un élément important de l'appui au développement du réseau

Prédiction de la composition des grandes graminées: des étalonnages multi-espèces peuvent-ils être utilisés pour l'extrapolation ?

Les grandes graminées comme le sorgho, le mil ou la canne à sucre sont largement cultivées dans les régions tropicales, où leurs sous-produits (pailles, tiges …) sont souvent utilisés pour l'alimentation animale mais ont aussi un potentiel comme sources de matière organique pour les sols, ou d'énergie. Il est important de pouvoir déterminer leur composition chimique, leur dégradabilité et leur valeur nutritive. Des étalonnages SPIR spécifiques existent pour les espèces majeures (e.g. maïs). Les sous-produits et les espèces moins communes ont un potentiel d'utilisation dans certains contextes, mais il n'y a souvent pas d'étalonnage robuste pour prédire leur composition. Cette étude a été réalisée pour évaluer la possibilité de prédire des échantillons peu typiques à partir d'une base de données rassemblant plusieurs espèces. (Résumé d'auteur

Faecal near infrared spectroscopy (FNIRS) a support tool to manage small ruminants

Faecal near infrared spectroscopy (FNIRS) can be a good technique to predict functional properties like intake or in vivo digestibility of forages by small ruminants. Data were collected from 108 different digestibility indoor and outdoor trials in Senegal and France carried out from 1993 to 2013 on sheep fed with a large variety of forage species. Faecal samples were scanned by a Foss NIRSystem 5000 monochromator. Calibrations were established on indoor trials samples and performed using the modified partial least square (mPLS) procedure to estimate dry or organic matter (DMI, OMI, g/kg metabolic weight, BW0.75) intake, in vivo dry and organic matter (DMD, OMD,%) digestibility. The derived standard errors of calibration (SEC) and coefficients of determination (R2cal) were 6.52 g/kg BW0.75 and 0.81 for DMI, 5.17 g/kg BW0.75 and 0.86 for OMI, 1.50% and 0.93 for DMD and 1.95% and 0.88 for OMD, respectively. These values confirm the interest of the use of FNIRS as a tool to manage small ruminants. The results obtained show a good accuracy with values similar to other published results for intake and digestibility. Validation on outdoor trials samples show the difficulty to extrapolate the prediction of intake with limited samples number and only one pasture quality

Near infrared spectroscopy predictions on heterogeneous databases

Faced to heterogeneous database questions, a NIR user is often answered: "you should work on more homogeneous data-sets". Nevertheless, as heterogeneity and variability is widespread among lots of agriculture areas, it is not always possible to have subsets which are at the same time homogeneous and large enough for calibration. It is therefore interesting to try calibration on heterogeneous databases before saying it is impossible... The major objective was to compare different strategies for NIR predictions. On one hand, build models from a data-set comprising different data-subsets, and on another hand, compare them to models based on the 'pure' data-subsets. The raw materials studied there originated from industrially pre-processed plant residues and other tropical plant residues, potentially utilisable in composting. Pure data-sets were (i) wet grape skins, (ii) dry grape skins, (iii) de-oiled grape pips, (iv) coffee cake, (v) cocoa cake, (vi) olive pulp, (vii) tropical plant residues samples. The parameter measured were Organic Matter OM (n= 30 to 56) and Total Nitrogen Kjeldahl TN (n=32 to 55) for the pure data-sets. The compiled data-set comprised 327 OM and 283 TN analyses. All samples were dried (40°C) ground (<1 mm sieve) and scanned on a NIRS 6500 (Foss NIRSystems) in ring cups. Spectra were corrected with SNVD 2,5,5 (WIN-ISI) mathematical pre-treatment and calibrations were performed using a modified partial least square regression (mPLS, WIN-ISI). The equations for OM had Standard Errors of Calibration (SEC) varying from 0.28 to 0.75 g 100 g-1 d.m., for the pure data-sets, and 0.94 for the compiled data-set. The equations for TN had SEC varying from 0.10 to 0.15 g 100 d.m., and 0.16 g 100 g-1 d.m., respectively. Standard Errors of Cross Validation (SECV) for OM varied from 0.44 to 1.27 g 100 g-1 d.m., and 1.07 g 100 g-1 d.m., respectively, whereas those of TN varied from 0.12 to 0.49 g 100 g-1 d.m., and 0.17 g 100 g-1 d.m., respectively. The corresponding SD/SECV ratios for OM varied from 1.3 to 3.9 for the pure data-sets, and equalled 2.8 for the compiled data-set. Those of TN varied from 1.4 to 3.7, and 3.1, respectively. Calibrations on pure data-sets seem to perform slightly better than that of the compilation. Nevertheless, models developed on the global data-set (made by compilation of the subsets, thus heterogeneous) had an acceptable predictive capacity and this strategy is therefore very useful. [Résumé d'auteur

Can a single wavelength be a proxy for lipid content in plant samples of Mediterranean shrublands?

In the framework of studies on the nutritional value of rangeland plants, it can sometimes be useful to have an indication of the lipid content of samples, although it is not a major component of plant vegetative parts. Heterogeneous databases gathering very diverse plants or plant parts require a very large number (several hundreds) of chemical analyses for calibration of chemical composition, so that lipids are generally not considered in such databases. The present study intends to seek for a proxy of lipid content as a single wavelength in the NIR spectrum. The objective is not to predict lipid content, but to be able to rank the plants according to their lipid content. The study is based on samples from a study on Mediterranean shrubby rangelands (“garrigue”) with mixed vegetation: grasses, shrubs, trees (Silué et al., 2016). About 250 samples from 60 species were collected in Corconne (southern France). Samples were dried mildly (55°C) and ground (1mm sieve). Spectra were collected on a FOSS NIRSYSTEM 5000 spectrometer with a wavelength range 1100-2500nm (2mm step). Then 30 samples were selected to represent the botanical diversity and the expected range of lipids. Lipid content was assessed by crude fat analysis by extraction with petroleum ether on Soxhlet. The approach was to correlate lipid content with absorption at individual wavelength, in order to identify wavelengths better representing lipids. This analysis was performed on raw spectra as well as on spectra pretreated with different derivation orders and smoothing options. The correlogram for 2nd derivative is shown as an illustration on Figure 1. The highest correlations obtained with the different pretreatements were R²=0.04 (at 1402nm) with raw spectra, 0.29 (at 1760nm) with 1st derivative, 0.63 (at 1772nm) with 2nd derivative, 0.64 (at 1782nm) with 3rd derivative. The correlation with wavelengths in the 1760-1780nm is high enough to provide a useful information on lipid content. When applied to the whole database, the lipid ranking identified the plants high in lipids (genera: Juniperus, Erica, Pinus, Rosmarinus, Asparagus, Dorycnium…) or low in lipids (Arbutus, Cistus, Asphodelus, Rubia, Hedera, Rhamnus …). However a proper validation with additional reference analyses has to be done. The high correlations with individual wavelengths suggest that a calibration of lipid content in such a database would be possible. However it would require many analyses, which is not relevant on the short term because accurate prediction of lipid content is not required. The proxy can help identifying the low / intermediate / high lipid plant parts, and can contribute to explain the feeding behavior of animals. It can also help for the selection of samples to be analyzed in the laboratory. A similar approach will be tested on fresh samples, since all samples were also scanned before drying: at this stage the volatile essential oils are still present in the samples and will provide more accurate information on secondary compounds present in the fresh plants. (Résumé d'auteur