Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes.

It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 - 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 - 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance.

Quantification of Dry Matter Content in Hass Avocado by Near-Infrared Spectroscopy (NIRS) Scanning Different Fruit Zones.

Accurate dry matter determination (DM) in Hass avocados is vital for optimal harvesting and ensuring fruit quality. Predictive models based on NIRS need to capture fruit DM gradient. This work aimed to determine the DM content in Hass avocado whole by NIRS scanning different fruit zones. Spectra were recorded for each zone of the fruit: peduncle (P), equator (E), and base (B). The calibration and validation included fruit from different orchards in two harvest cycles. The results show a DM gradient within the fruit: 24.47% (E), 24.68% (B), and 24.79% (P). The DM gradient was observed within the spectra using the RMSi (root mean square) criterion and PCA. The results show that at least one spectrum per fruit zone was needed to represent the variability within the fruit. The performances of the calibration using the whole set of data were R2: 0.74 and standard error of cross-validation (SECV) = 1.18%. In the validation stage using independent validation sets, the models showed similar performance (R2: 0.75, SECV 1.15%) with low values of the standard error of prediction (SEP): 1.62%. These results demonstrate the potential of near-infrared spectroscopy for high-throughput sorting of avocados based on their commercial quality.

Development and validation of near-infrared spectroscopy procedures for prediction of cassava root dry matter and amylose contents in Ugandan cassava germplasm.

BACKGROUND: Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter content (DMC) and the starch fraction of amylose content (AC). Accordingly, in the present study, near-infrared reflectance spectroscopy (NIRS) models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda. RESULTS: Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45 g 100g-1 , whereas, for amylose content, it ranged from 14 to 33 g 100g-1 . In the validation set, average DMC was 29.5 g 100g-1 , whereas, for amylose content, it was 24.64 g 100g-1 . For DMC, a modified partial least square regression model had regression coefficients (R2 ) of 0.98 and 0.96, respectively, in the calibration and validation set. These were also associated with low bias (-0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9 g 100g-1 to 1.06 g 100g-1 . For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to -0.03 and a ratio of performance deviation of 4.23 were observed. CONCLUSION: These findings confirm the robustness of NIRS in the estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy.

BACKGROUND: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near-infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. RESULTS: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2 ), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). CONCLUSION: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Hyperspectral imaging for the determination of relevant cooking quality traits of boiled cassava.

BACKGROUND: The purpose of this study was to investigate the potential of hyperspectral imaging for the characterization of cooking quality parameters, dry matter content (DMC), water absorption (WAB), and texture in cassava genotypes contrasting for their cooking quality. RESULTS: Hyperspectral images were acquired on cooked and fresh intact longitudinal and transversal slices from 31 cassava genotypes harvested in March 2022 in Colombia. Different chemometric methods were tested for the quantification of DMC, WAB, and texture parameters. Data analysis was conducted through partial least squares regression, K nearest neighbors regression, support vector machine regression and CovSel multiple linear regression (CovSel_MLR). Efficient performances were obtained for DMC using CovSel_MLR with, coefficient of multiple determination R p 2 = 0.94 $$ {R}_p^2=0.94 $$ , root-mean-square error of prediction RMSEP = 0.96 g/100 g, and ratio of the standard deviation values RPD = 3.60. High heterogeneity was observed between contrasting genotypes. The predicted distribution of DMC within the root can be homogeneous or heterogeneous depending on the genotype. Weak predictions were obtained for WAB and texture parameters. CONCLUSIONS: This study showed that hyperspectral imaging could be used as a high-throughput phenotyping tool for the visualization of DMC in contrasting cooking quality genotypes. Further improvement of protocols and larger datasets are required for WAB and texture quality traits. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Diversity and determinants of bitterness, astringency, and fat content in cultivated Nacional and native Amazonian cocoa accessions from Ecuador.

Cocoa (Theobroma cacao L.) is the only tree that can produce cocoa. Cocoa beans are highly sought after by chocolate makers to produce chocolate. Cocoa can be fine aromatic, characterized by floral and fruity notes, or it can be described as standard cocoa with a more pronounced cocoa aroma and bitterness. In this study, the genetic and biochemical determinants of sensorial notes and nonvolatile compounds related to bitterness, astringency, fat content, and protein content will be investigated in two populations: a cultivated modern Nacional population and a population of cocoa accessions collected recently in the Ecuadorian South Amazonia area of origin of the Nacional ancestral variety. For this purpose, a genome-wide association study (GWAS) was carried out on both populations, with results of biochemical compounds evaluated by near-infrared spectroscopy (NIRS) assays and with sensory evaluations. Twenty areas of associations were detected for sensorial data especially bitterness and astringency. Fifty-three areas of associations were detected linked to nonvolatile compounds. A total of 81 candidate genes could be identified in the areas of the association.

Integration of GWAS, metabolomics, and sensorial analyses to reveal novel metabolic pathways involved in cocoa fruity aroma GWAS of fruity aroma in Theobroma cacao.

Nacional is a variety of cocoa tree known for its "Arriba" aroma characterised mainly by fruity, floral, and spicy aromatic notes. In this study, the genetic basis of the fruity aroma of modern Nacional cocoa was investigated. GWAS studies have been conducted on biochemical and sensorial fruity traits and allowed to identify a large number of association zones. These areas are linked to both the volatile compounds known to provide fruity flavours and present in the beans before and after roasting, and to the fruity notes detected by sensorial analysis. Five main metabolic pathways were identified as involved in the fruity traits of the Nacional population: the protein degradation pathway, the sugar degradation pathway, the fatty acid degradation pathway, the monoterpene pathway, and the L-phenylalanine pathway. Candidate genes involved in the biosynthetic pathways of volatile compounds identified in association areas were detected for a large number of associations.

Two Main Biosynthesis Pathways Involved in the Synthesis of the Floral Aroma of the Nacional Cocoa Variety.

Theobroma cacao is the only source that allows the production of chocolate. It is of major economic importance for producing countries such as Ecuador, which is the third-largest cocoa producer in the world. Cocoa is classified into two groups: bulk cocoa and aromatic fine flavour cocoa. In contrast to bulk cocoa, fine flavour cocoa is characterised by fruity and floral notes. One of the characteristics of Nacional cocoa, the emblematic cocoa of Ecuador, is its aromatic ARRIBA flavour. This aroma is mainly composed of floral notes whose genetic and biochemical origin is not well-known. This research objective is to study the genetic and biochemical determinism of the floral aroma of modern Nacional cocoa variety from Ecuador. Genome-Wide Association Study (GWAS) was conducted on a population of 152 genotypes of cocoa trees belonging to the population variety of modern Nacional. Genome-Wide Association Study was conducted by combining SSR and SNP genotyping, assaying biochemical compounds (in roasted and unroasted beans), and sensory evaluations from various tastings. This analysis highlighted different areas of association for all types of traits. In a second step, a search for candidate genes in these association zones was undertaken, which made it possible to find genes potentially involved in the biosynthesis pathway of the biochemical compound identified in associations. Our results show that two biosynthesis pathways seem to be mainly related to the floral note of Nacional cocoa: the monoterpene biosynthesis pathway and the L-phenylalanine degradation pathway. As already suggested, the genetic background would therefore appear as largely explaining the floral note of cocoa.

Concurrent starch accumulation in stump and high fruit production in coffee (Coffea arabica).

In coffee, fruit production on a given shoot drops after some years of high yield, triggering pruning to induce resprouting. The timing of pruning is a crucial farmer's decision affecting yield and labour. One reason for fruit production drop could be the exhaustion of resources, particularly the non-structural carbohydrates (NSC). To test this hypothesis in a Coffea L. arabica agroforestry system, we measured the concentrations of NSC, carbon (C) and nitrogen (N) in leaves, stems and stumps of the coffee plants, 2 and 5 years after pruning. We also compared shaded vs full sun plants. For that purpose, both analytical reference and visible and near infrared reflectance spectroscopy (VNIRS) methods were used. As expected, concentrations of biochemical variables linked to photosynthesis activity (N, glucose, fructose, sucrose) decreased from leaves to stems, and then to stumps. In contrast, variables linked more closely to plant structure and reserves (total C, C:N ratio, starch concentration) were higher in long lifespan organs like stumps. Shading had little effect on most measured parameters, contrary to expectations. Concentrations of N, glucose and fructose were higher in 2-year-old organs. Conversely, starch concentration in perennial stumps was three times higher 5 years after pruning than 2 years after pruning, despite high fruit production. Therefore, the drop in fruit production occurring after 5-6 years was not due to a lack of NSC on plant scale. Starch accumulation in perennial organs concurrently to other sinks, such as fruit growth, could be considered as a 'survival' strategy, which may be a relic of the behaviour of wild coffee (a tropical shade-tolerant plant). This study confirmed that VNIRS is a promisingly rapid and cost-effective option for starch monitoring (coefficient of determination for validation, R2val = 0.91), whereas predictions were less accurate for soluble sugars, probably due to their too similar spectral signature.

Near-infrared spectroscopy applications for high-throughput phenotyping for cassava and yam: A review.

The review aimed to identify the different high-throughput phenotyping (HTP) techniques that used for quality evaluation in cassava and yam breeding programmes, and this has provided insights towards the development of metrics and their application in cassava and yam improvements. A systematic review of the published research articles involved the use of NIRS in analysing the quality traits of cassava and yam was carried out, and Scopus, Science Direct, Web of Sciences and Google Scholar were searched. The results of the review established that NIRS could be used in understanding the chemical constituents (carbohydrate, protein, vitamins, minerals, carotenoids, moisture, starch, etc.) for high-throughput phenotyping. This study provides preliminary evidence of the application of NIRS as an efficient and affordable procedure for HTP. However, the feasibility of using mid-infrared spectroscopy (MIRS) and hyperspectral imaging (HSI) in combination with the NIRS could be further studied for quality traits phenotyping.

Dataset of chemical and near-infrared spectroscopy measurements of fresh and dried poultry and cattle manure.

Combined with multivariate calibration methods, near-infrared (NIR) spectroscopy is a non-destructive, rapid, precise and inexpensive analytical method to predict chemical contents of organic products. Nevertheless, one practical limitation of this approach is that performance of the calibration model may decrease when the data are acquired with different spectrometers. To overcome this limitation, standardization methods exist, such as the piecewise direct standardization (PDS) algorithm. The dataset presented in this article consists of 332 manure samples from poultry and cattle, sampled from farms located in major regions of livestock production in mainland France and Reunion Island. The samples were analysed for seven chemical properties following conventional laboratory methods. NIR spectra were acquired with three spectrometers from fresh homogenized and dried ground samples and then standardized using the PDS algorithm. This important dataset can be used to train and test chemometric models and is of particular interest to NIR spectroscopists and agronomists who assess the agronomic value of animal waste.

Multi-block classification of chocolate and cocoa samples into sensory poles.

The present study aims at developing an analytical methodology which allows correlating sensory poles of chocolate to their chemical characteristics and, eventually, to those of the cocoa beans used for its preparation. Trained panelists investigated several samples of chocolate, and they divided them into four sensorial poles (characterized by 36 different descriptors) attributable to chocolate flavor. The same samples were analyzed by six different techniques: Proton Transfer Reaction-Time of Flight-Mass Spectrometry (PTR-ToF-MS), Solid Phase Micro Extraction-Gas Chromatography-Mass Spectroscopy (SPME-GC-MS), High-Performance Liquid Chromatography (HPLC) (for the quantification of eight organic acids), Ultra High Performance Liquid Chromatography coupled to triple-quadrupole Mass Spectrometry (UHPLC-QqQ-MS) for polyphenol quantification, 3D front face fluorescence Spectroscopy and Near Infrared Spectroscopy (NIRS). A multi-block classification approach (Sequential and Orthogonalized-Partial Least Squares - SO-PLS) has been used, in order to exploit the chemical information to predict the sensorial poles of samples. Among thirty-one test samples, only two were misclassified.

A comparison study of five different methods to measure carotenoids in biofortified yellow cassava (Manihot esculenta).

The most commonly used method for measuring carotenoid concentration is high-performance liquid chromatography (HPLC). Nevertheless, easier, quicker, and less costly proxy methods exist. We aimed to determine the diagnostic performance of several proxy methods: the spectrophotometer, iCheck Carotene, and near-infrared spectroscopy using both a desktop (dNIRS) and a portable (pNIRS) device for the measurement of total carotenoid concentration (TCC) and all-trans-ß-carotene concentration (trans-BC) in 30 fresh cassava (Manihot esculenta Crantz) storage roots in comparison with HPLC. The spectrophotometer presented the highest predictability for TCC, followed by iCheck, dNIRS, and pNIRS. The dNIRS showed the highest predictability and agreement for trans-BC. The pNIRS showed the poorest repeatability and greatest underestimations compared with HPLC. The agreement between all methods was lower for higher carotenoid concentration, with the exception of the spectrophotometer. According to our results, and for screening purposes, the measurement of carotenoids in fresh cassava roots can be carried out by spectrophotometer, iCheck Carotene and NIRS methods depending on the availability of equipment.

Assessment of cocoa (Theobroma cacao L.) butter content and composition throughout fermentations.

Cocoa fermentation is a crucial step for the development of cocoa aroma and precursors of high quality cocoa and by-products. This bioprocess has been studied for years to understand cocoa chemical changes but some matters concerning changes in fat content remain that are investigated in this work. Changes in the quantity (free and total fat), extractability and composition of cocoa butter were assessed in samples from Madagascar, the Dominican Republic and Ecuador. Increases in free fat content were highlighted in samples from each origin thanks to the use of the 'soxtec' solvent method, which preserves the integrity of the butter. A 4.71% increase in free fat was measured in the Ecuadorian samples fermented for 144 h. Conversely, total fat content remained stable throughout fermentation. Protein and polyphenol contents decreases were linked to fat content augmentation by a strong negative interaction. Triglyceride and total and linked fatty acid kinetics (0 to 6 days) of the butter remained statistically stable during fermentation, as did unsaponifiable matter. The origin of fermentation had a predominant and significant impact on composition, revealed by PCA. This work underlines and explains the importance of fermentation process in improving yield of fat that can be extracted while preserving the composition of this cocoa butter. This study highlights an interaction in cocoa unfermented or partially fermented beans. This phenomenon causes butter content retention but is slowly broken after 72 h fermentation. Therefore, fermentation appears to be also necessary to enhance the cocoa butter content extracted from the nibs.

Rapid analyses of dry matter content and carotenoids in fresh cassava roots using a portable visible and near infrared spectrometer (Vis/NIRS).

Portable Vis/NIRS are flexible tools for fast and unbiased analyses of constituents with minimal sample preparation. This study developed calibration models for dry matter content (DMC) and carotenoids in fresh cassava roots using a portable Vis/NIRS system. We examined the effects of eight data pre-treatment combinations on calibration models and assessed calibrations on processed and intact root samples. We compared Vis/NIRS derived-DMC to other phenotyping methods. The results of the study showed that the combination of standard normal variate and de-trend (SNVD) with first derivative calculated on two data points and no smoothing (SNVD+1111) was adequate for a robust model. Calibration performance was higher with processed than the intact root samples for all the traits although intact root models for some traits especially total carotenoid content (TCC) (R2c = 96%, R2cv = 90%, RPD = 3.6 and SECV = 0.63) were sufficient for screening purposes. Using three key quality traits as templates, we developed models with processed fresh root samples. Robust calibrations were established for DMC (R2c = 99%, R2cv = 95%, RPD = 4.5 and SECV = 0.9), TCC (R2c = 99%, R2cv = 91%, RPD = 3.5 and SECV = 2.1) and all Trans ß-carotene (ATBC) (R2c = 98%, R2cv = 91%, RPD = 3.5 and SECV = 1.6). Coefficient of determination on independent validation set (R2p) for these traits were also satisfactory for ATBC (91%), TCC (88%) and DMC (80%). Compared to other methods, Vis/NIRS-derived DMC from both intact and processed roots had very high correlation (>0.95) with the ideal oven-drying than from specific gravity method (0.49). There was equally a high correlation (0.94) between the intact and processed Vis/NIRS DMC. Therefore, the portable Vis/NIRS could be employed for the rapid analyses of DMC and quantification of carotenoids in cassava for nutritional and breeding purposes.

Near infra-red characterization of changes in flavan-3-ol derivatives in cocoa (Theobroma cacao L.) as a function of fermentation temperature.

Flavan-3-ols were successfully extracted from cocoa by the Fast-Prep device and analyzed by HPLC-DAD, and their identifications were confirmed by injection of authentic standards. (-)-Epicatechin was the most abundant component with an average of 9.4 mg/g dried cocoa powder. More than 700 cocoa samples were used to calibrate the NIRS. An efficient calibration model was developed to accurately determine any flavan-3-ol compound of ground dried cocoa beans (SEP = 2.33 mg/g in the case of total flavan-3-ols). This performance enabled NIRS to be used as an efficient and easy-to-use tool for estimating the level of targeted compounds. The analysis of the PLS loadings of the model and pure epicatechin spectra gave proof that NIRS was calibrated on an indirect strong correlation resulting in the changes in flavan-3-ols during fermentation and their interaction with some major components, such as proteins. Total flavan-3-ol concentration fell from an average of 33.3 mg/g for unfermented samples to an average of 6.2 mg/g at the end of fermentation. Changes in flavan-3-ol content were dependent upon the origin and highly correlated to the fermentation level expressed as the sum of temperatures (average R(2) = 0.74), a good marker of the fermentation process and of the heterogeneity of the batch.

Diversity of cacao trees in Waslala, Nicaragua: associations between genotype spectra, product quality and yield potential.

The sensory quality and the contents of quality-determining chemical compounds in unfermented and fermented cocoa from 100 cacao trees (individual genotypes) representing groups of nine genotype spectra (GG), grown at smallholder plantings in the municipality of Waslala, Nicaragua, were evaluated for two successive harvest periods. Cocoa samples were fermented using a technique mimicking recommended on-farm practices. The sensory cocoa quality was assessed by experienced tasters, and seven major chemical taste compounds were quantified by near infrared spectrometry (NIRS). The association of the nine, partially admixed, genotype spectra with the analytical and sensory quality parameters was tested. The individual parameters were analyzed as a function of the factors GG and harvest (including the date of fermentation), individual trees within a single GG were used as replications. In fermented cocoa, significant GG-specific differences were observed for methylxanthines, theobromine-to-caffeine (T/C) ratio, total fat, procyanidin B5 and epicatechin, as well as the sensory attributes global score, astringency, and dry fruit aroma, but differences related to harvest were also apparent. The potential cocoa yield was also highly determined by the individual GG, although there was significant tree-to-tree variation within every single GG. Non-fermented samples showed large harvest-to-harvest variation of their chemical composition, while differences between GG were insignificant. These results suggest that selection by the genetic background, represented here by groups of partially admixed genotype spectra, would be a useful strategy toward enhancing quality and yield of cocoa in Nicaragua. Selection by the GG within the local, genetically segregating populations of seed-propagated cacao, followed by clonal propagation of best-performing individuals of the selected GG could be a viable alternative to traditional propagation of cacao by seed from open pollination. Fast and gentle air-drying of the fermented beans and their permanent dry storage were an efficient and comparatively easy precondition for high cocoa quality.

Near infrared spectroscopy for high-throughput characterization of Shea tree (Vitellaria paradoxa) nut fat profiles.

The Shea tree (Vitellaria paradoxa) is a major tree species in African agroforestry systems. Butter extracted from its nuts offers an opportunity for sustainable development in Sudanian countries and an attractive potential for the food and cosmetics industries. The purpose of this study was to develop near-infrared spectroscopy (NIRS) calibrations to characterize Shea nut fat profiles. Powders prepared from nuts collected from 624 trees in five African countries (Senegal, Mali, Burkina Faso, Ghana and Uganda) were analyzed for moisture content, fat content using solvent extraction, and fatty acid profiles using gas chromatography. Results confirmed the differences between East and West African Shea nut fat composition: eastern nuts had significantly higher fat and oleic acid contents. Near infrared reflectance spectra were recorded for each sample. Ten percent of the samples were randomly selected for validation and the remaining samples used for calibration. For each constituent, calibration equations were developed using modified partial least squares (MPLS) regression. The equation performances were evaluated using the ratio performance to deviation (RPD(p)) and R(p)(2) parameters, obtained by comparison of the validation set NIR predictions and corresponding laboratory values. Moisture (RPD(p) = 4.45; R(p)(2) = 0.95) and fat (RPD(p) = 5.6; R(p)(2) = 0.97) calibrations enabled accurate determination of these traits. NIR models for stearic (RPD(p) = 6.26; R(p)(2) = 0.98) and oleic (RPD(p) = 7.91; R(p)(2) = 0.99) acids were highly efficient and enabled sharp characterization of these two major Shea butter fatty acids. This study demonstrated the ability of near-infrared spectroscopy for high-throughput phenotyping of Shea nuts.

Quantification of kavalactones and determination of kava (Piper methysticum) chemotypes using near-infrared reflectance spectroscopy for quality control in vanuatu.

Kava ( Piper methysticum Forst f., Piperaceae) has anxiolytic properties and the ability to promote a state of relaxation without the loss of mental alertness. The rapid growth of the nutraceutical market between 1998 and 2000 has been stopped by a ban in Europe and Australia because of some suspicion of liver toxicity. It is now important to develop a fast, cheap, and reliable quality test to control kava exports. The aim of this study is to develop a calibration of the near-infrared reflectance spectroscopy (NIRS) using partial least-squares (PLS) regression. Two hundred thirty-six samples of kava roots, stumps, and basal stems were collected from the Vanuatu Agricultural Research and Technical Centre germplasm collection and from four villages. These samples, representing 45 different varieties, were analyzed using NIRS to record their absorption spectra between 400 and 2500 nm. A set of 101 selected samples was analyzed for their kavalactone content using HPLC. The results were used for PLS calibration of the NIRS. The NIRS prediction of the kavalactone content and the dry matter were in agreement with the HPLC results. There were good correlations between these two series of results, and coefficients ( R (2)) were all close to 1. The measurements were reproducible and had repeatability on par with the HPLC method. The NIRS system has been calibrated for the six major kavalactone content measurements, and it is suggested that this method could be used for quality control in Vanuatu.

Effects of shade on the development and sugar metabolism of coffee (Coffea arabica L.) fruits.

Coffee fruits grown in shade are characterized by larger bean size than those grown under full-sun conditions. The present study assessed the effects of shade on bean characteristics and sugar metabolism by analyzing tissue development, sugar contents, activities of sucrose metabolizing enzymes and expression of sucrose synthase-encoding genes in fruits of coffee (Coffea arabica L.) plants submitted to full-sun (FS) and shade (SH) conditions. Evolution of tissue fresh weights measured in fruits collected regularly from flowering to maturation indicated that this increase is due to greater development of the perisperm tissue in the shade. The effects of light regime on sucrose and reducing sugar (glucose and fructose) contents were studied in fresh and dry coffee beans. Shade led to a significant reduction in sucrose content and to an increase in reducing sugars. In pericarp and perisperm tissues, higher activities of sucrose synthase (EC 2.4.1.13) and sucrose-phosphate synthase (SPS: EC 2.4.1.14) were detected at maturation in the shade compared with full sun. These two enzymes also had higher peaks of activities in developing endosperm under shade than in full sun. It was also noted that shade modified the expression of SUS-encoding genes in coffee beans; CaSUS2 gene transcripts levels were higher in SH than in FS. As no sucrose increase accompanied these changes, this suggests that sucrose metabolism was redirected to other metabolic pathways that need to be identified.