A combined elementomics, metabolomics, and chemometrics approach as tools to identify the geographic origins of black pepper.

Trace metals (As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Sr, and Zn) in acid digests of some aromatic herbs and spices (basil, fennel, laurel, mint, oregano, rosemary, thyme, black pepper, cinnamon, coriander, and cumin) were determined quantitatively using ICP‐AES method. The highest concentrations (μg g−1 dry matter) of each of the 11 investigated metals were found as follows: As (0.42) and Pb (1.6) in rosemary samples; Cd (0.1) in basil; Co (0.62), Cu (12.13), and Zn (52.26) in oregano; Cr (2.95) and Mg (3110) in fennel, Fe (494) and Ni (7.61) in cumin; Mn (192) in black pepper; and finally Sr (60.68) in mint. Some chemometric techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least square discriminant analysis (PLS‐DA) were used on metallic concentrations data in an attempt to classify these aromatic herbs and spices. The unsupervised pattern recognition PCA and HCA models gave the same result about similarities and differences between the studied plant samples, and five clusters of similar aromatic herbs and spices samples were formed. In order to verify the results of this clustering, we used a supervised pattern recognition method called partial least square discriminant analysis (PLS‐DA). Classes were groups or clusters of similar plants obtained previously. A hierarchical model builder (HMB) based on four PLS‐DA models was used to simultaneous determination of the class of each sample. It was found that all samples were correctly classified by PLS‐DA in their original groups as determined by PCA and HCA.

The feasibility of applying NIR and FT-IR fingerprinting to detect adulteration in black pepper

The sustainability concerns of the developed world together with increased tourism activities and migration have stimulated the growth and expansion of domestic and international High Value Markets (HVMs) of black pepper. This study is an attempt to identify the transactional attributes and farmer capabilities that influence the smallholder farmers’ willingness to participate in black pepper HVMs and to examine the novel vertical integration mechanisms that connect them to HVMs. The study is based on primary data from randomly selected 198 smallholder farmers from eight prominent black pepper growing Agro-ecological Units (AEUs) in Kerala. The factors influencing the smallholder farmers’ willingness to participate in black pepper HVMs were analysed in logistic regression framework. The trend analysis revealed the growth in black pepper cultivation area as well as export at national level, while a stagnation trend in area was observed at state level. A negative trend was observed in production as well as productivity at national and state levels. The results shows that 32.83 per cent of smallholder farmers were willing to participate in black pepper HVMs. The farmer capabilities such as number of international linkages and digital literacy and transactional attributes such as asset specificity and number of verticals integrated were found to be significantly influencing smallholder farmers’ willingness to participate in HVMs. The study suggest that change agents may implement interventions to enhance the existing co-ordination mechanisms by exploiting the possibilities of digitisation and digital transformation. Also, efficient measures are required to protect farmers’ rights on the asset specific resources and to mitigate unfavourable agroecological transformations that hinder the production of site-specific assets that ensures sustainable value chain for black pepper high value products.

Black pepper is the most important and widely consumed spice in the world. Insects and diseases are the major concerns for black pepper production, among the many variables causing a decline in black pepper productivity. The major diseases that affect black pepper are foot rot (Phytophthora capsica) and anthracnose (Colletotrichum gloeosporioides). Early and precise diagnosis of diseases is crucial as it will enable the farmers to make timely interventions. In the current scenario, the application of image processing and deep learning techniques for the automatic detection of plant diseases emerges as a solution capable of promptly delivering interventions in time-sensitive scenarios, given its capacity to deliver performance approaching expert levels. Through this study, a deep learning-based approach has been developed to classify black pepper diseases based on leaf images. A model has been developed to detect the two major diseases of black pepper, i.e., anthracnose and foot rot diseases, using a Convolutional Neural Network (CNN) in Kerala, India. We have collected 2786 leaf images from different black pepper farms in Kerala, belonging to three classes of pepper diseases and one healthy leaf class in total. The classes of leaf diseases considered include an early and advanced stage of anthracnose, and Phytophthora foot rot. As the accuracy of the model increases with the number of images, different image augmentation techniques are performed on the originally captured images to generate a total of 18,234 images. The developed CNN model has been compared with eight other pre-trained state-of-the-art models, such as VGG16, VGG19, ResNet50, ResNet50V2, MobileNet V2, DenseNet121, InceptionV3, and Xception. The result shows that the developed CNN model attained a higher classification accuracy, precision, recall, and F1-score of 98.72%, 99.28%, 97.65%, and 98.66% respectively, on the unseen test dataset. A web application named “Black pepper Disease Identification App” for demonstrating the proposed model is developed. According to an overall performance assessment, deep learning is an effective technique for classifying black pepper diseases based on leaf images and identifying them in their early stages. Based on the overall performance, the newly developed model is found to be efficient in classifying the selected pepper diseases. The proposed model holds significant promise for enabling the timely identification of diseases with minimal human intervention. Its deployment benefits both researchers and farmers by facilitating prompt disease detection directly in the field.

Bengkulu Province has many potentials such as beaches, conservation forests, plantations and others. Plantations in Bengkulu consist of smallholders and private / BUMN businesses. Smallholder plantations include coffee, black pepper, palm, rubber, areca nut and other gardens. The results of this plantation are still traditionally processed, which is drying and drying in the open with the help of solar heat. This drying process is still being implemented where the quality is poorly controlled. This has caused the yield of the plantation in Bengkulu to be unknown to other regions. like coffee in the Bengkulu area which has a distinctive taste characteristic compared to other regions. Coffee that is processed is Robusta or Arabica coffee which is the mainstay of the people of Air Raman Village, Bengkulu Province. The processing process is dried in the yard of the house, field, roadside and others. This drying and processing process is easily polluted from dust, animal waste, uneven drying. And the erratic drying time causes the villagers to need a machine or garden processing technology. One technology is a rotary dryer. Rotary drying machines can be used by utilizing regional potential such as solar energy, wind energy, water energy and others. In addition to Coffee, this village also has garden products such as black pepper, Pinang and others where the process is the same as the coffee harvest. The advantage of this dryer is a shorter drying time, the use of solar energy can minimize operational costs for drying coffee and black pepper. The dried coffee and black pepper are free from animal waste, dust and various other types of garbage and the process of drying more than 4 days from drying is done manually / in the public yard. This is because the temperature inside the drying machine is rotary higher than the open air, the level of dryness of coffee and black pepper is produced evenly, because the temperature in the rotary dryer is equipped with a stirrer evenly spread so that the stirring process is no longer necessary. . The working mechanism of this tool is expected to help people producing coffee and black pepper to dry their crops so that the quality of coffee and black pepper is good and the selling value is high. The output of PPM activities The application of science and technology is a high selling value that will increase people's income, improve people's welfare to be better and also create new jobs for the community as a producer of rotary drying machines. The availability of appropriate technology for the design and construction of rotary drying machines. Scientific publications at the national level are published in the national journal Dharma Raflesia December 2018

This study aimed to assess the effect of natural antioxidants on storage time, physical and chemical properties of egg. A total of 200 laying ISA brown birds were distributed into eight dietary groups, each dietary group with 25 birds (5 birds per replicate). They were fed with roselle, black pepper, green tea, combine (roselle + black pepper + green tea) at 0.5 g/kg and 1.0 g/kg in basal diet respectively and a control feed. At the end of eight (8) weeks of feeding trial, twelve eggs were collected from each dietary group (six eggs per dietary group were analyzed for the internal and external properties while the remaining six were stored). Data collected include; Egg shape index, egg weight, shell thickness, membrane weight, yolk index, haugh unit, meat and blood spot, yolk colour, lipid profile, lipid peroxidation and proximate analysis. Data generated were subjected to Analysis of variance using the General Linear Model for factorial within a completely randomized design. The natural antioxidants significantly (P<0.05) improves the proximate composition of the poultry eggs. Both Green tea and Black pepper have significant effect (P<0.05) on the yolk percentage. Black pepper increases (P<0.05) the Haugh unit while it shows to be lower (P>0.05) in the combination of the antioxidants. The inclusion levels of the natural antioxidants on the internal and external quality of egg reveals that there were no significant (P>0.05) differences in the two inclusion level of 0.5 g/kg and 1.0 g/kg of feed but they have numerically higher values in the external parameters and internal parameters. Eggs stored for 4 weeks had the lowest value (P>0.05) for the proximate and lipid profiles, though there are no significant (P>0.05) differences in their values. The fresh eggs show high moisture content (P<0.05) but the value for nitrogen free extract is low (P>0.05) in the fresh eggs. The natural antioxidants improved significantly (P<0.05) the proximate composition of the poultry eggs, with the green tea having the highest value (P<0.05) for Moisture contents and CP, black pepper the highest (P<0.05) for CP Based on the result obtained from this study, the natural antioxidants (black pepper, green tea and roselle) in layers’ diet shows significant effects on the physical qualities of egg and the yolk color was also preferred with the inclusion of the natural antioxidants compared to the control. The chemical properties also deteriorate with the storage time. Natural antioxidants are hereby recommended for better and improved chemical qualities of eggs.

Black pepper (Piper nigrum L.) is one of the most important crops and global demand continues to increase, giving it a high export value. However, black pepper cultivation has been seriously affected by a number of pathogenic diseases. Among them, “quick wilt” caused by Phytophthora sp., “slow decline” caused by Fusarium sp., and root-knot nematode Meloidogyne sp. have a serious negative effect on black pepper growth and productivity. There have been different chemical and biological methods applied to control these diseases, but their effectiveness has been limited. The aim of this research was to evaluate different combinations of rhizosphere bacteria and endophytic bacteria isolated from black pepper farms in the Central Highland of Vietnam for their ability to suppress pathogens and promote black pepper growth and yield. Formula 6, containing the strains Bacillus velezensis KN12, Bacillus amyloliquefaciens DL1, Bacillus velezensis DS29, Bacillus subtilis BH15, Bacillus subtilis V1.21 and Bacillus cereus CS30 exhibited the largest effect against Phytophthora and Fusarium in the soil and in the roots of black pepper. These bio-products also increased chlorophyll a and b contents, which led to a 1.5-fold increase of the photosynthetic intensity than the control formula and a 4.5% increase in the peppercorn yield (3.45 vs. 3.30 tons per hectare for the control). Our results suggest that the application of rhizosphere and endophytic bacteria is a promising method for disease control and growth-promotion of black pepper.

Viscosity of alkaline suspensions of ground black and white pepper samples: An indication or an identification of high dose radiation treatment?

An experiment was conducted to assess the effect of natural antioxidants (black pepper, green tea, roselle and their combinations) on meat quality of broiler chickens. A total of 270 1 - day old Arbor Acre broiler chicks were randomly distributed into nine treatments of three replicates each (10 birds in each replicate) in a 2 x 5 factorial arrangement for 2 inclusion levels (0.5g and 1.0g per kg of feed) of natural antioxidants (Control (CT), Green tea (GT), Roselle (RS), Black pepper (BP) and combination (CM) of the 3 antioxidants). At the end of the feeding trial (at 8 weeks), nine birds per treatment were immobilized, slaughtered, dressed, weighed and cut into primal cuts. The growth (initial and final body weight gain, average daily feed intake and weight gain, and feed conversion) and blood assay (haematology and serum biochemistry) of the birds were monitored while the breast and thigh meat cuts were subjected to physico-chemical and sensory analysis. The result indicated that, among examined natural antioxidants, BP improved the bird’s live weight. High Density Lipoprotein value was highest (p<0.05) in control and closely followed by birds on GT, CM, BP and RS. The lowest blood (p<0.05) cholesterol was recorded in RS which was closely followed by GT and CM. Carcass evaluation showed that birds fed BP had better (p<0.05) live weight (2.05kg) and highest acceptability (p<0.05) for organoleptic properties. The breast meat weight was also highest (p<0.05) in BP. It was concluded that the natural antioxidants increased live weight, improved performance and reduced abdominal fat. RS reduced blood cholesterol while RS, CM (GT + RS + BP) inclusion improved serum total protein of broiler chickens. Inclusion of natural antioxidant in the diets of broiler is hereby advocated for achieving optimum broilers performance and meat quality.

In the presented work, several data fusion and machine learning approaches were explored within the frame of the data combination for various measurement techniques in biomedical applications. For each of the measurement techniques used in this work, the data was ana-lyzed by means of machine learning. Prior to applying these machine learning algorithms, a specific preprocessing pipeline for each type of data had to be established. These pipelines made it possible to standardize the data and to decrease sample-to-sample variations which originate from the instability of devices or small deviations in the sample preparation or measurement routine. The preprocessed data sets were used for various analyses of biological samples. Separate data analyses were performed for microscopic images, Raman spectra, and SERS data. However, this work mainly focused on the application of data fusion methods for the analy-sis of biological tissues and cells. To do so, different data fusion pipelines were constructed for each task, depending on the data structure. Both low-level (centralized) and high-level (distributed) data fusion approaches were tested and investigated within in this work. To demonstrate centralized and distributed data fusion, two examples were implemented for tissue investigation. In both examples, a combination of Raman spectroscopic and MALDI spectrometric data were analyzed. One example demonstrated centralized data fusion for the analysis of the chemical composition of a mouse brain section, and the other example employed distributed data fusion for liver cancer detection. Other data fusion examples were demonstrated for cell-based analysis. It was demonstrated that leukocyte cell subtype identification can be improved by a centralized data fusion of Raman spectroscopic data and morphological features obtained from microscopic images of stained cells. The last example presented in this work demonstrated a sepsis diagnostic pipeline based on the combination of Raman spectroscopic data and biomarkers. Besides the measured values, the demographic information of the patient was included in the analysis process for considering non-disease-related variations. During the construction of data fusion pipelines, such issues as unbalanced data contribu-tion, missing values, and variations that are not related to the investigated responses were faced. To resolve these issues, data weighting, missing data imputation, and the introduc-tion of additional responses were employed. For further improvement of analysis reliability, the data fusion pipelines and data processing routine were adjusted for each study in this work. As a result, the most suitable data fusion approach was found for every example, and a combination of the machine learning methods with data fusion approaches was demon-strated as a powerful tool for data analysis in biomedical applications.

Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach

Fast quantitative detection of black pepper and cumin adulterations by near-infrared spectroscopy and multivariate modeling

Black pepper (Piper nigrum L.) is an important spice crop with high economic value. However, its production is severely hampered by the oomycete pathogen, Phytophthora capsici. Integrative disease management strategies have been developed to control the pathogen, but the pathogen is in the phase of evolving its virulence. Absolute resistance against Phytophthora rot was not reported in black pepper germplasm. However, Piper colubrinum, a wild species is reported as resistant. Resistance proteins are involved in continuous surveillance of pathogen entry and activation of plant defense signalling pathways for an effective hypersensitive response to prevent pathogen invasion. In this study, a sequence-based homology approach using the conserved nucleotide-binding site (NBS) of known plant resistance genes was used to isolate Resistance Gene Analogs (RGA) and assess their transcript level during Phytophthora infection. The RGA transcript level was evaluated in resistant wild species (P. colubrinum), two moderately resistant black pepper genotypes (IISR Sakthi and 04-P24-1), and one susceptible genotype (Subhakara). The identified RGAs of black pepper were found to be of non-TIR R gene class with NBS motifs. The expressions of six PnRGAs were assessed employing qRT-PCR at different time points after challenging with highly aggressive Phytophthora isolate. The kinetics of differential expression post-infection with P. capsici indicates the differential timing and magnitude of pathogen recognition in resistant P. colubrinum and moderately resistant black pepper genotypes compared to susceptible genotype. In silico analysis revealed that differentially expressed P. nigrum RGAs function through ADP phosphorylation, which is a key process in pathogen recognition. The identification of P. nigrum RGAs induced by P. capsici should provide valuable information for cloning and characterization of resistance genes.

Facilitating the integration of primary traders into modern agricultural value chains, known as high-value markets (HVMs), presents a promising avenue for improving the sustainability of black pepper value chains in Kerala. Due to increased price volatility and risk exposure in trading conditions, primary traders are hesitant to prioritize quality aspects in their procurement decisions. A Best-Worst Scaling (BWS) experiment was employed with traders in the Agro-ecological units (AEUs) 12, 14, 15, 16, 17, 19, 20 and 21 of Kerala to comprehend their preferences regarding quality attributes that could promote sustained participation in HVMs. This study incorporates a unique aspect by examining the consistency of choices between the best and worst options, providing deeper insights into traders' decision-making processes and ensuring an accurate evaluation of preferences by minimizing biases. The choice experiment utilized fractional factorial and balanced incomplete block designs. The results indicate that traders predominantly favour a flexible, incentive-based pricing model and long-term formal relationships with buyers. Conversely, traders consistently rated premium payments and certification as the least favourable market attributes. Preference variations were influenced by traders' experience, income levels and location. The results reveal that primary traders possess the least understanding of factors that may facilitate their entry into HVMs. Our findings underscore the significance of educating traders on crucial market attributes that facilitate their participation in HVMs. Further research on their willingness to adapt to the requirements of HVMs to maximize the benefits to the system.

Unprecedented incidence of Covid 19, the global pandemic, made the marketing difficult for the spice growing farmers, especially for the black pepper farmers. Covid 19 has disrupted production, processing and marketing of black pepper at different points of the value chain. Since the spice industry is very labour intensive, it has got substantially due to lockdown followed by the Covid 19 incidence. It has affected the availability of labour, availability and proximity of the market place, share of produce sold through different marketing channels, quality and availability of agricultural inputs etc. These factors substantially affected the production and operational costs. This study has conducted among the black pepper farmers of different age, sex, income level, experience in farming, operational land holding etc. with in the geography of Kerala, a south Indian state where many of the farmers grow black pepper. This study is an attempt to investigate the challenges in marketing of black pepper by the farmers in Kerala after the incidence of Covid 19 pandemic. This study also provides recommendations to improve the marketing by overcoming different challenges raised after the incidence of the global pandemic, Covid-19.