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Soy beverage is becoming more and more popular because it is touted as a healthy food containing useful phytochemicals and is free from lactose and cholesterol. The purpose of this paper is to optimize the spray drying process parameters for obtaining soy beverage powder with good reconstitution and handling properties.

Pre-concentrated soy beverage was dried in a laboratory model spray dryer, and the effects of inlet air temperature (180-220°C), feed rate (20-40 ml/min) and feed solid content (15-25 per cent) on some physical parameters and reconstitution properties (wettability and dispersibility) of spray-dried soy beverage powders were investigated. Second order polynomial response surface model was selected for the analysis of data and optimization of the process.

Spray drying of soy beverage at different processing conditions resulted in powders with particle size (volume mean diameter) in the range of 86 to 156 µm. Dispersibility and wetting time of the spray-dried soy beverage powders was found to be in the range of 56 to 78 per cent and 30 to 90 s respectively, under various drying conditions. Inlet air temperature was found to be the main factor affecting most of the quality parameters, followed by solid content of the feed. Temperature significantly affected the wettability, dispersibility, colour parameters, particle size and flowability of the powder at p ≤ 0.01. Lower temperature and higher feed solid content produced bigger-sized powder particles with better handling properties in terms of flowability and cohesiveness. A moderate inlet air temperature (196°C), higher feed solid content (24 per cent) and lower feed rate (27 ml/min) were found suitable for drying of soy beverage.

The study implied the possibility of producing powder from soy beverage using the spray-drying method and optimized drying conditions for obtaining soy beverage powder with good reconstitution properties.

The finding of this study demonstrated for the first time how the inlet air temperature, feed solid content and feed rate during spray-drying influenced different quality parameters of soy beverage powder. Further, an optimized drying condition has been identified.

This paper aims to study the color change kinetics of lac dye in response to pH and food spoilage metabolites (ammonia, lactic acid and tyramine) for its potential application in intelligent food packaging.

UV-Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A528/A488) with time using zero- and first-order reaction kinetics. An indicator was prepared by incorporating lac dye in agarose membrane to validate the result of study for monitoring quality of raw milk.

Dye was orange-red in acidic medium (pH: 2 to 5) and exhibited absorbance peak at 488 nm. It turned purple in alkaline medium (pH: 7 to10) and exhibited absorbance peak at 528 nm. The change in absorbance ratio with pH followed zero-order model. Acid dissociation constant (pKa) of dye was found to be 6.3. Color change of dye in response to ammonia and tyramine followed zero-order reaction kinetics, whereas for lactic acid, the first-order model was found best. In the validation part, the color of the indicator label changed from purple to orange-red when the milk gets spoiled.

The study opens a new application area for lac dye. The results suggest that lac dye has potential to be used as an indicator in intelligent food packaging for detection of spoilage in seafood, meat, poultry and milk.

The purpose of this paper was to study the color change kinetics of lac dye in response to aldehydes, carbon dioxide and other food spoilage metabolites for its potential application in intelligent food packaging.

UV–Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A₅₂₈/A₄₈₈) with time using zero and first-order reaction kinetics. Lac dye-based indicator was prepared to validate the result of study for monitoring quality of strawberries.

Lac dye was orange-red in acidic medium and purple in alkaline medium. Color change of dye in response to benzaldehyde followed zero-order reaction kinetics, whereas for carbon dioxide first-order model was found best. No color change of dye solution was observed for alcohols, ketones and sulfur compounds. In the validation part, the color of the indicator label changed from purple to orange when the strawberries spoiled.

The study expands application area for lac dye as sensing reagent in intelligent food packaging for spoilage or ripeness detection of fruits and vegetables.

Human capital is a portfolio of rich skills that the labour possesses. Human capital has attracted significant attention from scholars. Nevertheless, empirical findings on the utility of human capital have often been divided. To address the research gap in the literature, the authors attempt to understand how human capital plays a significant role in financial development and economic growth nexus.

The authors rely on secondary data published by the World Bank. The authors use econometric tools such as the autoregressive distributive lag (ARDL) model and related statistical tests to study the relationship between human capital, India's financial growth and gross domestic product (GDP) growth.

Study findings suggest that human capital and financial development contribute significantly to economic growth. Further, the authors found that human capital has a positive and significant moderating effect on the path of joining financial development and economic growth.

The study contributes to the human capital debate. Despite the rich body of literature, the study based on World Bank data confirms the previous findings that investment in human capital is always useful for the financial and economic growth of the nation.

This paper reveals some unique findings regarding effect of financial development and economic growth nexus which opens the window of new dimension to think about their nexus. It also provides a different pathway to foster the economic growth by using human capital and financial development as together, especially in India.

Data envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.

DEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.

The proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.

The use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

Since the post-liberalization era, a noticeable structural change and transition in employment have unfolded within the Indian economy. Hence, the purpose of this paper is to understand employment transition and elucidate the evolving dynamics of rural economies and employment patterns from agriculture to more productive non-agricultural sectors. Additionally, the study investigates the underlying causes of socioeconomic disparities and their repercussions on employment trends.

To address the aforementioned issues, this study utilised secondary data from labour surveys conducted by the National Sample Survey Organisation spanning from 2004–05 to 2023. Initially, the study computed the magnitude of employment in both agriculture and non-agriculture sectors. Subsequently, the distribution of non-agricultural labour across various socioeconomic characteristics was estimated. Furthermore, a logistic regression model was employed to evaluate the impact of socioeconomic factors on employment choices. Finally, Fairlie’s decomposition model was applied to elucidate workers’ decisions to engage in non-agricultural sectors.

The study reveals a significant rise in rural non-agricultural employment, from 98.4 m in 2004–05 to 193.3 m in 2023, indicating changing job preferences. Notably, the construction and trade sectors emerge as significant drivers of this trend. However, self-employment and casual labour persist, highlighting job vulnerability. Additionally, women and marginalised individuals with low levels of education and socioeconomic status lag behind in non-agricultural employment.

This study makes a significant contribution by offering a thorough analysis of the employment transition from agriculture to non-agriculture over a span of two decades. It provides valuable insights into the evolving dynamics of employment trends.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2023-0904.