Kernel Composition Research Articles

Characterization of sunflower seed and oil wax ester composition by GC/MS, a final evaluation

The hull of the sunflower is a ligneous tissue that protects the embryo and cotyledons, and it is rich in wax esters (WEs). WEs are extracted from whole sunflower seeds together with the oil, conferring an undesirable turbidity on this product which must be removed through cooling and filtration for commercialisation. The analysis of sunflower oil WEs is hampered by the presence of significant amounts of sterol esters that co-elute with them in chromatography. Thus, we established here different methods based on GC/MS to analyse the WE content and composition of sunflower hulls, kernels and oil, and that of industrially crystallised sunflower WEs. Hence, using a combination of MS scanning and specific ion monitoring it was possible to quantify and characterise sunflower WEs, overcoming interference by other lipids in the sample. The results revealed that sunflower WEs were formed by saturated species from C40 to C60. The use of the new method for the analysis of WEs from species other than sunflower was also discussed.

Effect of Ecotype and Environment on Oil Content, Fatty Acid, and Sterol Composition of Seed, Kernel, and Epicarp of the Atlas Pistachio

The Atlas pistachio fruits are an important source of food due to their high fat content and medicinal interest in arid lands. For a better use, it is necessary to understand the diversity and distribution of lipids in their fruit. The purpose of the present study is to determine oil content, fatty acid, and phytosterol composition using gas chromatography of three ecotypes in Algeria during two consecutive years. The seed oil contents ranged from 32 to 47.8% depending on both year and ecotype. Unsaturated fatty acids (FA) represent at least 71% of total FA. The fruit contains a considerable level of phytosterols. The highest level of oil content and unsaturated FA were observed in the fruit of Bechar regardless of the part of the fruit. The study indicated that oil quality of the Atlas pistachio seed varies according to genotype and environmental conditions, and the findings could help breeders for diversity and selection program management.

Multiple kernel fusion: A novel approach for lake water depth modeling

Multiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling, lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR), the support vector regression (SVR), back propagation neural network (BPNN) and auto regressive (AR) models. More specifically, while each individual input parameter fails to provide an accurate prediction for lake water depth modeling, an optimal combination of all input parameters incorporating the groundwater level, streamflow, precipitation and evaporation via a multiple kernel learning approach enhances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE = 0.098 m; R2 = 0.987; NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems.

Direction of arrival estimation using Gaussian process interpolation

Gaussian processes (GP) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous due to its ability to denoise measurements, and for its prediction of likely field outcomes given a certain field coherence, or in GP terminology, a kernel. While there are many design options for a coherence function, in this study we examine using the radial basis function kernel, the physically based plane wave kernel, and a composition of plane wave kernels representing a certain angular interval of directions. The composite kernel is relevant in ocean acoustics where it is often the case that arrivals can only be within a narrow direction of arrival. We demonstrate that an array sampled with spacing larger than a half wavelength can benefit from GP interpolation, giving less root mean squared error.

Biobriquette: A Mixture of Palm Kernel Shell and Coconut Shell, an Indonesian Study Case

Nowadays, the world energy demand is very high while the fossil energy source decreases continuously.Biobriquette presents as environmentally friendly alternative energy from biomass. One of the potentialresources of biomass for biobriquette is agricultural waste, such as palm kernel shells and coconut shells.The availability of these wastes is abundant but has not been utilized optimally. Thus, biobriquette productioncan solve two problems at once, namely energy and environmental problems. The objective of this study isto determine the optimum mass ratio of palm kernel shell and coconut shell which has the quality in termsof calorific value, and proximate characteristics (moisture content, ash content, volatile substances, andfixed carbon) qualified the SNI (Indonesia National Standard) specification (No. 01-6235-2000) forbiobriquette. The mixing variations of the coconut shell and palm kernel were 100: 0, 75: 25, 50: 50, 25: 75,and 0: 100. The results of the quality analysis have shown that all samples have met all SNI criteria and thesample with 100% kernel composition was the best composition.

Fatty acids composition and physical properties of stones and kernels from different peach cultivars as biomarker of origin and ripening time

The peach stones and kernels are easily available biowaste which could be useful for the extraction of nutritionally important compounds such as fatty acids. Except in industry, characterization of stones and kernels could be useful in pomology to describe different cultivars, and for selecting new parents in a breeding program. A total of 25 samples of stones and kernels from various peach cultivars that differed in origin and ripening time, but growing in the same climatic conditions, were characterized by fatty acids composition and physical properties. This work confirmed that unsaturated fatty acids (oleic and linoleic fatty acids) were the most represented in peach kernel oil and their content depended of peach genotype. Additionally, the fatty acids in combination with length, weight, and moisture of peach kernels could be used as a parameter of authenticity assessment. This research may contribute for the peach cultivar discrimination and recommendation of cultivars/genotypes with high kernel quality which could be used for the extractions of oil rich in unsaturated fatty acids and further use in food, pharmaceutical and cosmetic industry. Besides, selected cultivars could be used in breeding programs, for creating new genotypes for oil production.

Evaluation of the Heterogeneity of Wheat Kernels as a Traditional Model Object in Connection with the Asymmetry of Development

Wheat is one of the most important crops in the world, providing food for most of the world’s population. Wheat seeds are a popular model object for many experiments to evaluate various factors that improve germination or protect against various adverse stressful effects. Based on the high significance of increasing the productivity of this cereal crop and the applicability of this object, a detailed statistical evaluation of wheat grain (kernel) morphometry was carried out to assess the asymmetry of parameters of this ideal model. Depending on the location of the kernels in the spikelet of a wheat spike, there was a significant asymmetry between the right and left cheeks of the kernels located closer or further from the center of the spikelet. The expressiveness of asymmetry, and consequently, the kernel deformation was higher in the lower kernels of the spikelet. The degree of symmetry; that is, the similarity of the two halves (cheeks) and the kernel as a whole, was higher in kernels located higher in the spikelet. It seems that the reason for this phenomenon lies in the mechanical nature of kernel deformation. The ultrastructure of A-type and B-type starch grains in the central part of the kernel had significant differences between the upper and lower kernels, which indicated in favor of a high probability of differences by the composition and quality of kernels of the same variety when assessed separately. Uniform development of kernels and smaller differences between them may reveal more valuable genotypes in the future, provided their steady reproduction under adverse conditions of a changing climate.

A novel high-throughput hyperspectral scanner and analytical methods for predicting maize kernel composition and physical traits

Large-scale investigations of maize kernel traits important to researchers, breeders, and processors require high throughput methods, which are presently lacking. To address this bottleneck, we developed a novel flatbed platform that automatically acquires and analyzes multiwavelength near-infrared (NIR hyperspectral) images of maize kernels precisely enough to support robust predictions of protein content, density, and endosperm vitreousness. The upward facing-camera design and the automated ability to analyze the embryo or abgerminal sides of each individual kernel in a sample with the appropriate side-specific model helped to produce a superior combination of throughput and prediction accuracy compared to other single-kernel platforms. Protein was predicted to within 0.85% (root mean square error of prediction), density to within 0.038 g/cm3, and endosperm vitreousness percentage to within 6.3%. Kernel length and width were also accurately measured so that each kernel in a rapidly scanned sample was comprehensively characterized.

Comparative Studies on the Physicochemical Characteristics and Lipid Contents of Desert Date (Balanites aegyptiaca (L.) Del) Kernel and Pulp Oils

Aims: This work is aimed at investigating physicochemical parameters and compositions of fatty acid, phospholipid and sterol of desert date (Balanites aegyptiaca) kernel and pulp.
 Study Design: Balanites aegyptiaca fruit is one of the oldest feed-stocks in Africa of which little or no attention has been given to it. The plant plays a diverse cultural and traditional role in different societies. Therefore, it is very important to explore more about the chemical composition of the kernel and pulp oils of Balanites aegyptiaca; since it is currently attracting considerable research interest as a result of its diverse beneficial properties.
 Methodology: The physicochemical parameters, fatty acids, phospholipids and phytosterols of B. aegyptiaca seed and pulp oils have been analyzed and compared with the standards and that of conventional oil for easy assessment of their suitability for nutritional and industrial applications.
 Results: The results of some physicochemical parameters of kernel and pulp oils were acid value (26.35 and 15.60 mg KOH/g), peroxide value (3.82 and 5.90 meq/kg), saponification value (162.40 and 198.60 mg KOH/g), iodine value (55.20 and 142.50 mg of I/100 g), specific gravity (0.93 and 0.92), kinematic viscosity (2.12 and 1.65 St) and refractive index (1.41 and 1.39), respectively. The most concentrated fatty acids were palmitic acid (14.53%) < linoleic acid (35.65%) < oleic acid (38.27%) for the kernel oil while that of pulp oil were linolenic acid (8.21%) < oleic acid (16.80%) < palmitic acid (32.70%) < linoleic acid (33.56%). Arachidic, behenic, lignoceric and myristic acids were all present in small quantities with none of them recording up to 1.0% in either of the samples. Caprylic, capric and lauric acids were determined but not detected in both oils. The fatty acid composition of kernel and pulp oils contained a healthy mixture of all the types of saturated and unsaturated fatty acids. The value of polyunsaturated/saturated index (P/S) which is associated to the impact on human health was higher in the pulp oil (2.47). Phosphatidylcholine had the highest content in both oils that is 75.99 and 25.88 mg/100 g, respectively. The total values of phytosterols for kernel and pulp oils were 85.00 and 9.87 mg/100 g, respectively.
 Conclusion: Balanites aegytiaca kernel and pulp oils have the potential to substitute several materials used in manufacturing oil in the chemical and pharmaceutical industries. However, in order to extend usage, these oils should be refined in order to improve the colour and taste.

Deforestation by Afforestation: Land Use Change in the Coastal Range of Chile

In southern Chile, an establishment of a plantation-based forest industry occurred early in the industrial era. Forest companies claim that plantations were established on eroded lands. However, the plantation industry is under suspicion to have expanded its activities by clearing near-natural forests since the early 1970s. This paper uses a methodologically complex classification approach from own previously published research to elucidate land use dynamics in southern Chile. It uses spatial data (extended morphological profiles) in addition to spectral data from historical Landsat imagery, which are fusioned by kernel composition and then classified in a multiple classifier system (based on support, import and relevance vector machines). In a large study area (~67,000 km2), land use change is investigated in a narrow time frame (five-year steps from 1975 to 2010) in a two-way (prospective and retrospective) analysis. The results are discussed synoptically with other results on Chile. Two conclusions can be drawn for the coastal range. Near-natural forests have always been felled primarily in favor of the plantation industry. Vice versa, industrial plantations have always been primarily established on sites, that were formerly forest covered. This refutes the claim that Chilean plantations were established primarily to restore eroded lands; also known as badlands. The article further shows that Chile is not an isolated case of deforestation by afforestation, which has occurred in other countries alike. Based on the findings, it raises the question of the extent to which the Chilean example could be replicated in other countries through afforestation by market economy and climate change mitigation.

Physical fruit traits, proximate composition, fatty acid and elemental profiling of almond [Prunus dulcis Mill. DA Webb] kernels from ten genotypes grown in southern Morocco

This study was carried out with the aim to evaluate physical fruit traits, proximate composition, elemental content, and fatty acid profiling of almond kernels from ten genotypes (sweet and bitter) grown under various agro-ecological conditions (Tafraout, Igherm, Taliouine, Tiznit and Essaouira) in southern Morocco. During the ripening stage, samples (almond kernels) belonging to these genotypes were subjected to physical fruits measurements (weight, length, width, and thickness). Proximate composition determination (crude protein, crude oil, ash and moisture) was carried out on the powder of kernels for each genotype. Carbohydrates as well as energy value were calculated. Elemental profiling of almond kernels consisted of ten macro and microelements was determined as well as the fatty acids composition of almond extracted oil. The results of this study showed wide variations among the investigated genotypes in terms of crude protein (17.14 ± 2.14 to 25.12 ± 1.23 g/100 g), oil content (51.12 ± 2.99 to 56.26 ± 3.22 g/100 g), ash content (5.11 ± 0.45 to 6.79 ± 0.52 g/100 g), moisture content (2.55 ± 0.38 to 4.34 ± 1.16 g/100 g), carbohydrates (13.34 ± 1.54 to 18.59 ± 2.22 g/100 g) and energy value (549.80 ± 37.04 to 591.03 ± 38.56 kcal/100 g DM). A similar trend was observed in the case of mineral profiling. K (9796.08 ± 793.49 to 14 197.84 ± 1150.03 mg/kg) was the major mineral in all genotypes. This was followed by P (8190.75 ± 663.46 to 11 061.68 ± 895.96 mg/kg), Ca (3067.53 ± 248.47 to 5404.93 ± 437.79 mg/kg) and Mg (4002.85 ± 324.22 to 5101.72 ± 413.23 mg/kg), while Na, Cu, Fe, Zn, Mn and B were detected in small quantities. Our results revealed six fatty acids with a dominance of oleic acid, which varied widely among the studied genotypes and extraction technique. Solvent extraction performed better in terms of oil content. The studied genotypes showed a great pomological potential that could be exploited for their fruits such as for breeding purposes.

Mechanical Analysis of Composite with Reinforcement Sawdust and Palm Kernel Shells

In this study, the sawdust-palm kernel shellcrete composite was analysed. The sawdust-palm kernel shellcrete composite issue has been solved. Sawdust and palm kernel shells in weight varied from 100:0 to 90:10, 80:20 to 70:30, and 60:40 to 50:50 in composition. Urea formaldehyde (20% of the oven dry weight of agricultural waste) was used as a test binder. There were 300 m particles in the agricultural waste used in the process. A high water absorption capacity of aggregate materials and the need to build lightweight concrete with sufficient workability, strength, and durability led to the selection of these specific ratios of water to cement. In terms of yield strength (4.47 N/mm2), tensile strength (7.75 N/mm2), modulus of elasticity (2603 n/mm2), modulus of rupture (16.67 n/mm2), internal bond strength (0.54), thickness swelling (10.30 percent), water absorption (18.90 percent), and density (996.18 kg/m3), sawdust and palm kernel compositions performed best.

The Almond (Prunus dulcis): Chemical Properties, Utilization, and Valorization of Coproducts.

Almonds (Prunus dulcis) are one of the most consumed tree-nuts worldwide, with commercial production in arid environments such as California, Spain, and Australia. The high consumption of almonds is partly due to their versatile usage in products such as gluten-free flour and dairy alternatives as well as them being a source of protein in vegetarian diets. They contain high concentrations of health-promoting compounds such as Vitamin E and have demonstrated benefits for reducing the risk of cardiovascular disease and improving vascular health. In addition, almonds are the least allergenic tree nut and contain minute quantities of cyanogenic glycosides. Production has increased significantly in the past two decades with 3.12 billion pounds of kernel meat produced in California alone in 2020 (USDA 2021), leading to a new emphasis on the valorization of the coproducts (e.g., hulls, shells, skins, and blanch water). This article presents a review of the chemical composition of almond kernels (e.g., macro and micronutrients, phenolic compounds, cyanogenic glycosides, and allergens) and the current research exploring the valorization of almond coproducts.

Analysis of the Phytochemical Composition of Pomegranate Fruit Juices, Peels and Kernels: A Comparative Study on Four Cultivars Grown in Southern Italy.

The increasing popularity of pomegranate (Punica granatum L.), driven by the awareness of its nutraceutical properties and excellent environmental adaptability, is promoting a global expansion of its production area. This investigation reports the variability in the weight, moisture, pH, total soluble solids, carbohydrates, organic acids, phenolic compounds, fatty acids, antioxidant activities, and element composition of different fruit parts (juices, peels, and kernels) from four (Ako, Emek, Kamel, and Wonderful One) of the most widely cultivated Israeli pomegranate varieties in Salento (South Italy). To the best of our knowledge, this is the first systematic characterization of different fruit parts from pomegranate cultivars grown simultaneously in the same orchard and subjected to identical agronomic and environmental conditions. Significant genotype-dependent variability was observed for many of the investigated parameters, though without any correlation among fruit parts. The levels of phenols, flavonoids, anthocyanins, and ascorbic and dehydroascorbic acids of all samples were higher than the literature-reported data, as was the antioxidant activity. This is likely due to positive interactions among genotypes, the environment, and good agricultural practices. This study also confirms that pomegranate kernels and peels are, respectively, rich sources of punicic acid and phenols together, with several other bioactive molecules. However, the variability in their levels emphasizes the need for further research to better exploit their agro-industrial potential and thereby increase juice-production chain sustainability. This study will help to assist breeders and growers to respond to consumer and industrial preferences and encourage the development of biorefinery strategies for the utilization of pomegranate by-products as nutraceuticals or value-added ingredients for custom-tailored supplemented foods.

Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning.

It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction. Then, a GP was used as the function model. Recently, it was suggested that, albeit training with marginal likelihood, the deterministic nature of a feature extractor might lead to overfitting, and replacement with a Bayesian network seemed to cure it. Here, we propose the conditional deep Gaussian process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata and the exposed GP remains zero mean. Motivated by the inducing points in sparse GP, the hyperdata also play the role of function supports, but are hyperparameters rather than random variables. It follows our previous moment matching approach to approximate the marginal prior for conditional DGP with a GP carrying an effective kernel. Thus, as in empirical Bayes, the hyperdata are learned by optimizing the approximate marginal likelihood which implicitly depends on the hyperdata via the kernel. We show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. However, the conditional DGP and the corresponding approximate inference enjoy the benefit of being more Bayesian than deep kernel learning. Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning. We also address the non-Gaussian aspect of our model as well as way of upgrading to a full Bayes inference.

Elemental Analysis of Popular Zinc Rich Rice Varieties using ESEM and ICP-OES

A comparative study was carried out for elemental composition of grains of eight popular rice genotypes. The Environmental Scanning Electron Microscope (ESEM) setup was used to explore type and quantity of surface elements in various layers of rice grains. The main constituents are carbon and oxygen, with elements like Al and Si being prominently found on the husk. Rice kernels serve as staple food. We found that the top surface layer of whole rice kernel is rich in carbon compared to oxygen, but oxygen content increased in the internal area of kernel (endosperm in broken kernel) in all test genotypes. Further, our ESEM results showed that the husk of grain consists of more elements than the seed part. Though most of the above rice varieties are Zn and/or Fe rich, the ESEM facility could not identify them. ICP-OES analysis revealed Nagina-22 and R-RHZ-7 as highly zinc and iron rich rice varieties respectively. The knowledge of elemental composition of husk and whole kernel may pave the way for their effective uses in biofortification programme and as such for use as food, animal feed and industrial purposes.

The effect of fermentation by Bacillus subtilis and Aspergillus niger on the nutritional value of date palm kernels

This study was performed to investigate the effect of fermentation by Bacillus subtilis (B. subtilis) and Aspergillus niger (A. niger) and alkaline processing on the nutritional value and chemical composition of date palm kernels (DPK). DPK was fermented for 28 days under solid-state fermentation culture with two different microorganisms (B. subtilis and, or A. niger). Alkaline processing was performed by soaking DPK in NaOH solution for 24 hours. In this experiment, eight treatments were used: DPK (control), DPK fermented by B. subtilis, and A. niger separately or in combination, and processed with NaOH, alone or with B. subtilis or A. niger or both. Digestibility and gas production of fermented and processed DPK were performed using the batch culture and gas production techniques. The results showed that there was an increase in crude protein (CP) and a decrease in crude fiber concentrations due to the fermentation of DPK with B. subtilis and A. niger (P < 0.05). Total phenol content in fermented DPK (FDPK) with B. subtilis and A. niger was significantly lower than the control. Gas production significantly increased in FDPK compared to control and NaOH-treated samples (p <0.05). All treatments significantly increased the in vitro digestibility of DM (IVDOD) and OM (IVOMD) compared to the control (p < 0.01). Among the treatments, simultaneous fermentation of DPK with B. subtilis and A. niger had the most significant effect on increasing the microbial CP (MCP) and its efficiency (EMCP; p <0.01).

Comparative analysis of the chemical composition and size of starch granules in grain between diploid and tetraploid sweetcorn cultivars

Background. Improving the taste and nutritional value of sweetcorn using genetic selection techniques is an important trend. The tetraploid sugary maize cultivar ‘Baksanskaya sakharnaya’ (k-23426) has a number of advantages in terms of taste and marketability over traditional diploid varieties.The purpose of the research was to identify the distinctive biochemical characteristics that affect the taste of this cultivar in comparison with the diploid reference (cv. ‘Rannyaya Lakomka’, с-1775).Materials and methods. The differences in phenotypic traits between diploid (2n) and tetraploid (4n) maize plants were assessed. Infrared spectroscopy was used to study the difference in the biochemical composition of kernels in terms of protein, starch and oil content. Accumulation of metabolites in a dry kernel was studied using gas–liquid chromatography with mass spectrometry.Results. Comparison of the phenotypic traits in 2n and 4n sweetcorn plants showed that 4n had a better ear structure, protein and oil content. Analysis of metabolites showed that quantitative indicators of chemical components changed in kernels of the 4n sweetcorn without changing the qualitative composition. The chemical composition of the metabolites in 4n sweetcorn grain was characterized by an increase in the synthesis of most organic acids and protein amino acids, phosphoric acid, fatty acids, some monosaccharides, phenolic compounds, and phytosterols. A decrease in the content of most polyhydric alcohols, disaccharides, trisaccharides, some monosaccharides, and phenolic compounds was observed. An inhibitory effect of a su2 gene dose on the size of starch granules was found. Conclusion. The studies showed the advantage of the 4n sweetcorn over the 2n one due to its improved ear structure and biochemical composition, which positively affected the taste qualities.

Economic and Nutritional Values of Walnut: The Main Reason for Development of Walnut in Uzbekistan

This work systematically addresses the chemical composition, use, production and consumption of walnuts. The protein amino acid composition, fatty acid composition and vitamin contents of walnut kernel was analyzed. It was shown that walnut oils may be used to treat inflammatory processes and ulcers of the stomach and intestines and its therapeutic effect is associated with the presence of linoleic acid. The kernel cake remaining after the extraction of the oil contains a great deal of protein with high levels of essential amino acids. A delicious, useful and nutritious halva can be prepared from kernel cake. The kernel can also be used in the confectionery industry for the production of fillings for sweets, cakes, and chocolate. The review also describes the dynamics of walnut production, in the USA, Ukraine, Turkey, Iran, China, India, Brazil, Chile and the countries of the European Union as the largest producers. It also discusses that in Uzbekistan, walnuts were historically collected on farm plots as natural walnut forests, small plantations and household plots, but now there is an increased interest in improving walnut crops using the achievements of science and technology, as well as agrotechnical methods of cultivation. Using the work of scientists at the Uzbek Research Institute of Horticulture, Viticulture and Wine making the volume of production of this valuable product will increase in near future.

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness. Recently, automatic kernel composition methods provide not only accurate prediction but also attractive interpretability through search-based methods. However, existing methods suffer from slow kernel composition learning. To tackle large-scaled data, we propose a new sparse approximate posterior for GPs, MultiSVGP, constructed from groups of inducing points associated with individual additive kernels in compositional kernels. We demonstrate that this approximation provides a better fit to learn compositional kernels given empirical observations. We also provide theoretically justification on error bound when compared to the traditional sparse GP. In contrast to the search-based approach, we present a novel probabilistic algorithm to learn a kernel composition by handling the sparsity in the kernel selection with Horseshoe prior. We demonstrate that our model can capture characteristics of time series with significant reductions in computational time and have competitive regression performance on real-world data sets.