
==== Front
Ultrason Sonochem
Ultrason Sonochem
Ultrasonics Sonochemistry
1350-4177
1873-2828
Elsevier

S1350-4177(24)00303-1
10.1016/j.ultsonch.2024.107055
107055
Original Research Article
Valorization of tomato processing by-products: Predictive modeling and optimization for ultrasound-assisted lycopene extraction
Kuvendziev Stefan a
Lisichkov Kiril a
Marinkovski Mirko a
Stojchevski Martin a⁎
Dimitrovski Darko a
Andonovikj Viktor bc
a Faculty of Technology and Metallurgy, Ss. Cyril and Methodius University in Skopje, Rugjer Boskovic 16, 1000 Skopje, North Macedonia
b Jožef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia
c Jožef Stefan International Postgraduate School, Jamova Cesta 39, 1000 Ljubljana, Slovenia
⁎ Corresponding author.
30 8 2024
11 2024
30 8 2024
110 10705511 7 2024
30 7 2024
29 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a distinctive red color. The main source of lycopene is tomato crops. This bioactive component can be successfully isolated from tomato processing waste, commonly called tomato pomace, mostly made from tomato skins, seeds, and some residual tomato tissue. The main investigative focus in this work was the application of green engineering principles in each stage of the optimized ultrasound-assisted extraction (UAE) of enzymatically treated tomato skins to obtain functional extracts rich in lycopene. The experimental plan was designed to determine the influence of studied operating parameters: enzymatic reaction time (60, 120, and 180 min), extraction time (0, 5, 10, 15, 30, 60, and 120 min), and temperature (25, 35 and 45 ℃) on lycopene yield. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions. An artificial neural network (ANN) model was developed and trained for predictive modeling of the closed extraction system, with operating parameters used as input neurons and experimentally obtained values for lycopene content defined as the output neural layer. Applied ANN architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914) with a model deviation error for the entire data set of RMSE=5.3 mg/kg. The k-Nearest Neighbor algorithm was introduced to predict lycopene yield using experimental key features: operating temperature, extraction time, and time of enzymatic treatment, split into training and testing sets with an 85/15 ratio. The model interpretation was conducted through the SHAP (SHapley Additive exPlanations) methodology.

Keywords

Tomato bio-waste
Lycopene recovery
Ultrasound-assisted extraction
Enzymatic treatment
ANN modeling
KNN modeling
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pmc1 Introduction

Contemporary trends in green process engineering promote the implementation of the zero-emission and circular economy approach principles and the potential reconsideration of bio-waste or industrial processing by-products as secondary raw materials. Agricultural activities and food processing industries produce substantial amounts of bio-waste, including fruit and vegetable skins, seeds, crop residues, etc [1]. Biomass waste is a valuable raw material for isolating bioactive compounds and obtaining new products. The recovery of bioactive compounds from bio-waste is a sustainable and most often environmentally friendly process that includes waste utilization and producing relatively cheap products. Several by-products are generated during the production of various tomato-based products such as sauces, purees, ketchup, and juice. Tomato skins are often considered a waste in the conventional tomato processing industry. They are rich in essential bioactive compounds that have significant health benefits and are of interest in many industries. Carotenoids, polyphenols, vitamins, fibers, saponins, flavonoids, and phenolic acids are groups of bioactive compounds contained in tomato skins [2], [3].

Lycopene is a well-known carotenoid and the most abundant pigment in tomato skins, responsible for the red color of tomatoes. It has a long, straight-chain structure and contains a series of carbon–carbon conjugated double bonds. These conjugated double bonds are answerable for its antioxidant properties. In nature, lycopene exists in trans- and cis-isomeric forms. All-trans-lycopene or all-E-lycopene is the most common and naturally occurring bioactive form of lycopene. The tomato skins contain a mixture of lycopene isomers, but all-trans-lycopene is often used as a standardized form in research and supplements. Lycopene is naturally stored in the chromoplasts of plant cells. The wet tomato pomace comprises 40 % pulp, 33 % seed, and 27 % skin, while the dried form has 56 % skin and pulp, and 44 % seed [4], [5]. The main portion of the lycopene content is concentrated in the skin portion at about five times higher values than in the pulp. The investigated bioactive component – lycopene, has a proven impact on human health as an important antioxidant that has a strong association with minimization of the risk for growing chronic diseases such as cardiovascular diseases. It demonstrates anti-inflammatory and anti-cancer activity and improves bone and eye health. Therefore, lycopene is used in the cosmetic, pharmaceutical, and food industries as a raw material [6]. This carotenoid is sensitive to light, particularly ultraviolet light. The stability of lycopene also depends on temperature, exposure to oxygen, the applied extraction technique, solvents, etc. It is soluble in nonpolar organic solvents, such as n-hexane, chloroform, benzene, and petroleum ether, and insoluble in polar organic solvents. The extraction technique has a significant influence on the lycopene yield isolated from natural sources. Various conventional and modern extraction methods can be implemented for the separation of lycopene [7].

Recent research regarding the applicability of different extraction techniques for the isolation of lycopene from tomato waste suggests that conventional solvent extraction (CSE) and supercritical fluid CO2 extraction (SFE-CO2) techniques are considered as adequate and easily applicable. Considering the recent advances in chemical process engineering and modern trends in separation processes, the utilization of green solvents and reducing extraction time and energy consumption fit in the frames of green engineering. Hence, the application of ultrasound-assisted extraction (UAE) technique is favorable, especially considering its advantages over conventional separation techniques [8].

Ultrasound-assisted extraction (UAE) is a powerful separation method that uses high-frequency sound waves to enhance the extraction process of compounds from solid to liquid samples. During the solid–liquid UAE, various physical and chemical phenomena increase mass transfer from the solid to the liquid phase. These phenomena include acoustic cavitation, agitation, vibration, compression, expansion, and free radical formation. Physical effects that occur in the UAE are frequency-dependent [9]. When ultrasonic waves pass through a liquid medium, alternating cycles of compression and expansion are created. The distance between the molecules exceeds the critical distance and microscopic bubbles are formed when a high negative pressure is applied to the liquid. These bubbles typically form at low-pressure nodes in the wave, absorb energy from the ultrasonic waves, and grow over time. When the bubbles reach a critical size, they cannot absorb energy efficiently and rapidly collapse. This collapse generates intense localized forces, shockwaves, and microspaces with very high pressures [10]. Acoustic cavitation is a crucial mechanism in the ultrasound-assisted extraction of bioactive compounds from plant materials. The acoustic cavitation disrupts cell walls and reduces the particle size of the matrix. UAE is often employed in chemical and food industries, because of its advantages compared to conventional methods. UAE is an efficient, non-thermal process that reduces the extraction time and amount of solvent, and preserves the bioactivity of compounds. UAE is often used for the extraction of bioactive compounds, including lycopene, from natural matrices such as plants, fruits, and vegetables [11].

Enzymatic treatment of natural materials is a valuable method used in various industries to prepare raw materials for different processes, particularly those involving extraction and fermentation. Enzymes as biological molecules present in the living organisms’ cells in small amounts, are used to increase the rate of chemical reactions without being consumed. Compared with chemical catalysts, enzymes have many advantages such as high specificity, high catalytic efficiency, and adjustable activity [12]. Enzymes can break down the cell walls of plant materials and facilitate the extraction process of bioactive compounds. The use of cellulase, xylanase, and pectinase to hydrolyze and degrade the polysaccharide network can improve the isolation of components of interest from the cells. Considering the location and stability of lycopene, the UAE of lycopene from enzymatically treated tomato skins was found to be an interesting point of investigation [13].

Modeling of extraction processes is an essential phase in process engineering and involves the use of mathematical and computation models to describe the process and predict the yield of bioactive compounds from a raw material extracted under different process parameters. In the last decade, artificial neural networks (ANNs) are often used to predict and model non-linear processes, including UAE and other complex extraction techniques [14]. They are commonly used to approximate functions that depend on large input data, which are generally unknown. ANN is a system of interconnected neurons organized into layers and neurons send signals to each other. Connections between neurons have numerical weights and they can be unidirectional or bidirectional. ANN consists of an input layer, one or more hidden layers, and an output layer [15]. Neurons in the input layer receive the initial data, neurons in the output layer generate the predicted results, and neurons in the hidden layer process the input data through weighted connections and activation functions. Weights are adjusted during the training process to minimize prediction errors. Activation functions introduce non-linearity, allowing the neural network to model complex relationships in the data. Therefore, ANN is used to achieve predictive modeling of complex separation processes [16].

This study aimed to design an artificial neural network for predictive modeling of the UAE of lycopene from enzymatically treated tomato skins and determining the defined output of lycopene yield as a function of predetermined neural inputs: extraction time, extraction temperature, and enzymatic treatment time.

2 Materials and methods

The raw material, chemicals, and methods as well as the introduced analysis required for data curation are presented so that each analysis can be efficiently reproduced. Furthermore, extraction experiments were conducted in triplicate, and average values were used to analyze and discuss the designed separation system.

2.1 Sample preparation

Tomato skins from fresh ripe tomatoes were obtained from a local tomato processing industry in N. Macedonia. This raw material was used in this study as a potential secondary material rich in lycopene. To optimize the drying process, two techniques were studied. The first of the investigated techniques required that fresh tomato skins were dried in the dark and at room temperature for two days. Additionally, the drying process was completed in a laboratory dryer at 100 ℃ to final moisture of less than 10 % (method 1). In the second process, obtained skins were dried at room temperature to a moisture content of less than 10 % (method 2) [17], [18]. Dried tomato skins (8.24 % moisture) were ground using a commercial electric grinder (mean particle size of 0.78 mm), vacuumed in special vacuum bags, and stored in a refrigerator at a temperature of 4 ℃, without exposure to light (Fig. 1).Fig. 1 Tomato skins: dried to a moisture content of less than 10% (left), ground skins (right).

2.2 Enzymatic treatment of tomato skins

Enzymatic treatment is a common technique used to enhance the extraction of bioactive compounds, including lycopene, from natural sources. In this study, the influence on extraction efficiency of two commercially available enzyme preparations: enzymatic preparation 1 (Trenolin Rouge DF) and enzymatic preparation 2 (Lallzyme EX-V) were investigated related to lycopene extraction yield. These enzyme preparations, which contain pectinases, cellulases, and hemicellulases, are marketed to enhance color extraction during the maceration process of wine production [19]. The enzymatic treatment was performed using 0.1 g of tomato peel powder, mixed with 2 ml of distilled water, and 0.1 ml of enzyme solution (concentration according to manufacturer’s instructions) into 10 ml Eppendorf tubes. The tubes were mixed using a vortex mixer for 3 min, placed into a laboratory water bath at 25 ℃, and kept in dark conditions for the duration of 60, 120, and 180 min. To achieve reproducibility of experimental data and establish the validity of the elaborated interpretation of the observed process response (lycopene yield), all experiments were done in triplicate.

2.3 Conventional extraction of lycopene

The conventional solvent extraction (CSE) of lycopene was done using 5 ml of n-hexane (pro analysis, Alkaloid AD) added to the raw material and stirred using a vortex mixer for 5 min. The liquid extract was separated after centrifugation at 4000 rpm for 10 min, and the process was repeated three times for quantitative determination of lycopene presence in the total extract [20]. After extraction, the lycopene content was measured with UV/Vis spectroscopy. A reference sample with no enzyme addition was also prepared and the lycopene yield was determined. This experimental plan was designed to determine the quantitative presence of lycopene.

2.4 Ultrasound-assisted extraction of lycopene

The ultrasound-assisted extraction (UAE) of lycopene from tomato skins was performed in a laboratory-scale ultrasonic bath (Ei OOUR-RC, frequency 40 kHz). To avoid lycopene degradation by photooxidation and isomerization, the extraction was performed under dimmed light [21], [22]. Lycopene was extracted using n-hexane (pro analysis, Alkaloid AD) as a solvent. After the enzymatic treatment, 5 ml of n-hexane was added and the tube was placed in an ultrasonic bath. At the end of the extraction process, the sample was centrifuged following the procedure described previously. Ultrasound-assisted extraction was performed at a temperature of 25, 35, and 45 ℃ for a duration of 5, 10, 15, 30, 60, and 120 min. The ultrasound-assisted extraction of lycopene from enzymatic untreated tomato skins was also performed to obtain data required for comparative analysis of the experimental results as well as the optimization of the selected output – lycopene yield, and overall performance evaluation of the designed process.

2.5 Spectrophotometric determination of lycopene

The concentration of lycopene in the extract was determined at room temperature by using a UV/Vis spectrophotometer (Spectroquant Prove 600). The analyzed spectrum was recorded in the range of 300–600 nm using a quartz glass cuvette with a 10 mm optical path and Δλ = 1 nm. The baseline was automatically adjusted using n-hexane as a blank. The absorption spectra of the extract presented three typical peaks of lycopene at around 445, 472, and 502 nm (Fig. 2). To decrease the interference of other carotenoids, the peak obtained at 502 nm was used in further analysis [20], [21].Fig. 2 Characteristic UV/Vis peaks of lycopene samples obtained by UAE of experimental matrix-dried tomato skins.

The yield of lycopene,yL (mg/kg of dried and enzymatically treated tomato skins) was calculated by the equation:(1) yL=0.00312AVEDmS

where: A is the absorbance at 502 nm, VE is the volume of extract (ml), D is the dilution coefficient, and mS is the mass of the sample (kg).

2.6 ANN modeling of the UAE process

To select and design an appropriate artificial neural network (ANN) model for predictive modeling of the lycopene yield as a function of experimental variables in this research the MATLAB Neural Network Toolbox was employed. The model used is a multi-layer neural network model that has a feedforward backpropagation ANN architecture of one hidden layer in addition to the input and output layers. The input layer of the predefined ANN consists of the independent variables that were selected as operating parameters for the UAE process (extraction time, operating temperature, and enzymatic treatment time). The operational (hidden) layer contains 10 neurons as an optimal operating structure that was determined through the minimization of the mean squared error (MSE) function. The output layer has 1 neuron that represents the observed system response – lycopene yield. The described 3–10-1 architecture of the designed ANN model was assigned a sigmoid (hyperbolic tangent) transfer function to the operating and output layers. The input data matrix was randomly divided into training, validation, and test data sets. The designed ANN was consequently trained using the Levenberg-Marquardt training algorithm.

2.7 K-Nearest neighbors (KNN) regression model for the described UAE system

K-Nearest neighbors (KNN) regression model was developed to predict the yield of lycopene using three key features: temperature, extraction time, and enzymatic treatment. The KNN algorithm was chosen for this study due to its simplicity and effectiveness in capturing local patterns in the data. The introduction of this algorithm was backed with sufficient data obtained through the realization of the predetermined experimental plan and necessary data acquisition [23]. Lycopene UAE extraction yield was used as model target data of the created KNN model.

KNN is a non-parametric method that predicts the target value for a given data point by averaging the target values of the k-nearest neighbors in the feature space. Mathematically, the prediction for a new data point x can be expressed as [24]:(2) y^x=1k∑i∈Nk(x)yi

where Nk(x) denotes the set of k-nearest neighbors of x, and yi are the target values of these neighbors. The distance between data points was computed using the Euclidean distance metric, which is defined as:(3) dx,xi=∑j=1n(xj-xij)2

where xj and xij are the jth features of data points x and xi, respectively. For our implementation,k = 3 was selected, meaning that the prediction for a given data point was based on the average of the three nearest neighbors in the training set. This choice of k was determined based on cross-validation to balance bias and variance.

Before modeling, the data normalization was performed to ensure that each feature contributed equally to the model, as KNN is sensitive to the scale of input data. Specifically, the StandardScaler from the scikit-learn Python module was utilized to standardize the features by removing the mean and scaling to unit variance:(4) x′j=xj-μjσj

where μj and σj are the mean and standard deviation of the jth feature, respectively. This step is crucial for KNN, which relies on distance calculations; without normalization, features with larger ranges could disproportionately influence the results.

To evaluate the model’s performance, the dataset was split into training and testing sets with an 85/15 ratio. This split ensured that the model was trained on the majority of the data while still being tested on a significant portion to evaluate its generalization ability [25]. The training set was used to fit the KNN model, while the testing set was used for evaluating the model’s performance.

The KNN model’s performance was evaluated using the root mean squared error (RMSE), which measures the square root of the average squared differences between the predicted and actual values. The RMSE is defined as:(5) RMSE=1n∑i-1n(y^i-yi)2

where y^i are the predicted values, yi are the actual values, and n is the number of data points in the test set. The calculated RMSE on the test data was 230 mg/kg, indicating the model’s prediction accuracy.

To interpret the model and understand the contribution of each feature to the predictions, the SHAP (SHapley Additive exPlanations) methodology was employed. SHAP values provide a unified measure of feature importance by attributing the prediction to each feature in a manner consistent with cooperative game theory. Using the SHAP Kernel Explainer, SHAP values for the test set were generated, allowing visualization and interpretation of the impact of each feature on the model’s predictions.

3 Results and discussion

Results that were acquired through the designed experimental plan based on the physicochemical characterization of the raw material, performing the defined extraction process, and the introduction of the KNN and ANN modeling are presented graphically and discussed after detailed performance analysis.

3.1 Design of the pre-extraction treatment process

Two methods were used to dry tomato skins, from which lycopene was extracted and compared. According to the results (Fig. 3A), it can be concluded that the drying method significantly influences the lycopene content. The method that obtained skins without a heating step showed an 82 % higher lycopene content. This protocol was subsequently used in all future experiments. These results are attributed to the potential degradation of lycopene during prolonged heat treatment above 50 ℃, which outweighs the isomerization from trans- to cis-forms [11], [12], [13]. This effect can be attributed to thermal isomerization and degradation of lycopene content when the raw material is exposed to higher operating temperatures. Therefore, to minimize the effect of thermal and photo- degradation and isomerization of lycopene and to avoid reduced extraction efficiency, raw material matrixes were subjected to the described drying method – at room temperature under dark.Fig. 3 The influence of the (A) drying process, (B) enzyme, and (C) enzymatic treatment on lycopene content.

The reason for obtaining more lycopene when using an enzyme preparation before extraction is that the enzymes present in the preparation (pectinases, cellulases, hemicellulases, etc.) participate in the breaking of the cell wall and facilitate the release of lycopene and accelerate the mass transfer parameters, which is very difficult to achieve by conventional extraction with organic solvent. Enzyme preparation 1 is a pectinase based system, whereas the enzyme preparation 2 is a macerative enzyme comprised of pectinase, cellulase and hemicellulase.

Two enzyme preparations were tested and compared to a reference sample (no enzymatic treatment) using lycopene content obtained by the conventional extraction method. Data (Fig. 3B) indicates that enzymatic preparation 2 had slightly higher lycopene content than enzymatic preparation 1; however, there was no statistically significant difference between the two. Both preparations showed a significant increase in lycopene content compared to the reference sample. Enzymatic preparation 2 was chosen for further experiments due to its availability and longer shelf life.

The influence of enzymatic treatment duration on lycopene content was also examined. The results (Fig. 3C) revealed a linear increase in lycopene content as enzymatic reaction duration ranged from 60 to 180 min. However, the increase was only 13.5 % between 60 and 180 min, indicating that the lycopene yield increases only slightly with the duration of enzyme treatment.

The primary experimental plan for quantitative determination of lycopene presence is graphically presented in Fig. 4. The columns in the first three segments represent the concentration of lycopene after three consecutive extractions with n-hexane.Fig. 4 Quantitative extraction of lycopene through CSE and UAE.

In the case of extraction by simple mixing (without enzymatic treatment or ultrasound), most of the lycopene was extracted during the second extraction. This is likely because the intact plant cell walls hindered the mass transfer of lycopene into the extraction solution. In the presence of ultrasonication, the concentrations of lycopene in the first two extractions were similar. The ultrasound significantly affected the extraction efficiency by increasing the lycopene yield. However, it was only after enzymatic treatment that extraction achieved significantly higher lycopene concentrations in the first extraction, reaching nearly 80 % of the total amount obtained after all three extractions. Ultimately, the combination of enzymatic treatment and ultrasound extraction resulted in the most efficient lycopene extraction from the tomato skins which provides the essential engineering justification of the designed separation system. Therefore, this extraction system was established for further investigations necessary to generate data for predictive modeling.

3.2 Ultrasound-assisted extraction of lycopene coupled with enzymatic treatment

Experimental results regarding the lycopene yield by application of UAE from the studied raw material are presented in Table 1. The lycopene extraction yield was determined as mg of lycopene obtained from 1 kg of dried and enzymatically treated tomato skins.Table 1 Lycopene yield at different operating conditions.

Extraction time (min)	Temperature [℃]	Enzymatic treatment (min)	
0	60	120	180	
Lycopene yield [mg/kg]	
0	25	0	0	0	0	
5	25	348	621	661	700	
10	25	460	748	805	832	
15	25	480	796	859	907	
30	25	441	590	640	638	
60	25	470	678	728	754	
120	25	508	718	770	803	
0	35	0	0	0	0	
5	35	405	675	718	770	
10	35	474	797	857	910	
15	35	505	866	947	931	
30	35	439	460	540	601	
60	35	480	690	737	789	
120	35	515	726	781	816	
0	45	0	0	0	0	
5	45	419	733	788	819	
10	45	506	931	1002	1060	
15	45	576	1018	1067	1120	
30	45	519	670	714	769	
60	45	565	985	1060	1130	
120	45	593	1034	1105	1190	

The analytical interpretation of presented results depicting the process dynamics at different values for the operating temperature (25, 35, and 45 ℃) suggest optimal process performance for an extraction time of 15 min for each of the experimental curve series observed as a desirable local maximum for lycopene content (480, 505, 576 mg/kg, respectively). Enzyme treatment of the raw material was introduced to enhance mass transfer within the closed extraction system, resulting in higher lycopene recovery during the UAE process. Obtained results are given in Fig. 5.Fig. 5 UAE of lycopene-rich extracts from dried tomato skins.

The analytical interpretation of experimentally obtained values (Table 1 and Fig. 5) results in a conclusion that the enzymatic treatment of the studied raw material matrixes using the predefined enzymes has a significant positive influence on the lycopene recovery. The results observed as a desirable local maximum (15 min of extraction time) for lycopene content determined for the UAE process on treated samples (1018, 1067, and 1120 mg/kg) represent a significant increase compared to the lycopene yield obtained at the same operating conditions excluding enzymatic treatment (480, 505, 576 mg/kg) for UAE on untreated raw material. This conclusion is additionally confirmed by the assessment of the maximal values regarding lycopene yield results.

Interpretation of experimentally obtained results regarding the lycopene extraction yield achieved by UAE indicates that ultrasonic cavitation effect coupled with the micro-bubbles implosion that generates microjets and macro-turbulence on the material:solvent contact surface accelerates the lycopene extraction associated mass transfer phenomena and interior lycopene release. This effect produces a rapid extraction phase in a kinetically controlled region of the dynamics curve (0–15 min). Although the UAE process for isolation of lycopene from enzymatically treated tomato skins generated a rapid isolation phase in the first 15 min, prolonged UAE resulted in decreased lycopene yield in the range of 15–30 min. The rapid extraction phase in the first 15 min results from the positive impact of the cavitation effect on the plant cells, causing increased mass transfer phenomena in the system:plant matrix-extraction solvent.

However, prolonged exposure to ultrasonication during the extraction process induces isomerization and degradation of the all-trans-lycopene thus reducing the lycopene yield in the second phase (15–30 min), which is also detected and reported by Lianfu and Zelong [26] and Xu and Pan [6]. The reason of this phenomenon might be due to the fact that the hydroxyl radicals produced by acoustic cavitation of ultrasound in extracts, with the presentation of a small amount of water, resulted in the decomposition of lycopene and hence, decrease on the observed extraction yield [6], [26]. This negative impact can be interpreted through the local temperature increase on the contact surface plant cells-extraction solvent and lycopene degradation under prolonged ultrasonication exposure resulting from cavitation micro-bubble collapse on the surface of the experimental matrix (plant material).

Still, the extraction curve enters diffusion controlled kinetics phase in the region of 30–120 min, where the negative effects of are outweighed by the positive impact of the extraction time and ultrasonication on the extraction process. The positive effect of ultrasonication that enables increased plant matrix-solvent mass transfer through the favorable cavitation effect and interior lycopene release continues after 30 min and results in a gradual increase of lycopene yield in the 30–120 min area of the process dynamics curve.

When compared to reported results regarding CSE of lycopene from tomato processing by-products, UAE system performance offers high values for extraction yield at significantly reduced processing. The moderate temperature under UAE had a positive effect on lycopene yield and could limit the degradation of all-trans-lycopene during extraction. These findings produce the conclusion that UAE offers considerable advantages over CSE when assessing the lycopene yield under optimal operating conditions [27], [28].

The comparative analysis of the employed UAE process and reported SFE of lycopene from tomato processing by-products indicates similar values regarding the lycopene extraction yield for both extraction techniques that are widely considered as contemporary extraction models in accordance with green process engineering principles. The UAE system investigated within the experimental analysis of this work produced satisfactory results regarding the lycopene extraction yield achieved at reduced operating time and moderate operating conditions, comparable to reported SFE yields. This analysis leads to the conclusion that high extraction yield was observed at optimal process values (reduced extraction time and mild operating temperature) under optimal techno-economical process aspects (low energy consumption and low operating costs) [27], [28].

3.3 KNN model interpretation and target data evaluation

The SHAP summary plot given in Fig. 6, which displays the distribution of SHAP values for each feature, offering insights into the importance and impact of each feature across all predictions. Additionally, individual SHAP value plots were generated to explain specific predictions, highlighting how particular features influenced the yield of lycopene in those instances.Fig. 6 SHAP diagram of the designed KNN model for the studied extraction system.

The SHAP summary plot provides a comprehensive view of the impact of each feature on the model’s output, specifically the lycopene yield. The plot indicates that the extraction time is the most significant feature influencing the model’s predictions. The wide spread of SHAP values along the x-axis for this feature demonstrates that both high and low extraction times substantially affect the predicted yield. Higher extraction times (indicated in red) generally have a positive impact on the yield, whereas lower extraction times (indicated in blue) tend to negatively influence the yield. The temperature is the second most influential feature. The SHAP values for temperature are spread around zero, showing a mix of positive and negative impacts on the lycopene yield. High temperatures (red) appear to enhance the yield, while low temperatures (blue) are associated with reduced yields. This suggests that maintaining optimal temperature conditions is crucial for maximizing the lycopene yield. The enzymatic treatment shows the least variability in SHAP values, indicating it has the smallest impact on the model’s predictions compared to the other two features. The narrower spread of SHAP values around zero for enzyme treatment time implies that variations in this feature have a relatively minor effect on the lycopene yield. Both high and low values of enzyme treatment time show limited impact, although there is a slight tendency for higher values (red) to positively influence the yield.

The optimal number of neighbors k for the KNN regression model was determined through empirical testing. We evaluated k values ranging from 1 to 10 and selected the value that minimized the RMSE on the test set. The optimal k was found to be 3, as it provided the best balance between bias and variance.

For model validation, we employed 5-fold cross-validation. This process involved dividing the dataset into five subsets, training the model on four subsets while validating it on the remaining subset, and repeating this process for each subset. This approach ensures that the model’s performance is robust and not overly reliant on a particular data split.

3.4 NN modeling the UAE coupled with enzymatic treatment

The process of creating an artificial neural network (ANN) takes place through three stages: training, validation, and testing of the model results (responses) produced by the resulting ANN initially designed using the experimentally obtained values. The generated neural network consists of one input, one operational (hidden), and one output (response) layer of neurons. The optimal number of neurons in the operational (hidden) layer of the neural network is determined by defining the minimum value of MSE to evaluate the deviation of the model outputs from the experimentally obtained results. The tansig transfer function for the operating layer of neurons generated an adequate fit of studied data. The structure of the developed ANN model is given in Fig. 7.Fig. 7 The architecture of the designed ANN predictive model.

The ANN model created to predict the yield of lycopene from enzymatically treated tomato skin matrices employing UAE was trained using the corresponding experimental data. The created neural network consists of one input, one operational (hidden), and one output (response) layer of neurons. The characteristics of the created model are given in Table 2.Table 2 ANN model characteristics.

Characteristic	Specification	
Algorithm	Feed-forward back-propagation	
Minimized error function	MSE	
Training	Levenberg-Marquardt	
Training type	Supervised	
Input layer neurons	No transfer function	
Hidden layer neurons	Hyperbolic tangent transfer function	
Output layer neurons	Hyperbolic tangent transfer function	
Number of neurons in the input layer	3	
Number of neurons in the hidden layer	10	
Number of neurons in the output layer	1	

The experimental results comprised the data matrix necessary to build an ANN predictive model that depicts the lycopene yield as a functional dependency from the selected input neurons (parameters). The resulting optimal artificial neural network has a structural model described through neural weights and biases given in Table 3.Table 3 Weights and biases of the designed ANN predictive model for UAE.

Weights	Biases	
Variable	Value	Variable	Value	Variable	Value	Variable	Value	
w11	−0.85743	w16	0.044473	w1	−0.12048	b11	3.2534	
w21	−0.12039	w26	−2.0789	w2	1.8031	b12	−3.709	
w31	−3.1501	w36	−4.0381	w3	0.060608	b13	−2.2692	
w12	0.0098934	w17	−0.0057817	w4	0.80929	b14	0.043651	
w22	−8.173	w27	3.6449	w5	0.32733	b15	0.0022933	
w32	0.074111	w37	−0.41209	w6	−0.36746	b16	2.1041	
w13	1.6957	w18	−1.5614	w7	2.3035	b17	1.0351	
w23	1.8055	w28	−2.1888	w8	−0.041182	b18	−2.0381	
w33	−0.075458	w38	−1.3627	w9	−3.7789	b19	−11.177	
w14	0.028485	w19	0.034807	w10	−0.13842	b10	−4.1057	
w24	−1.4952	w29	−10.4848	−	−	b21	−3.2037	
w34	0.91343	w39	0.031327	−	−	−	−	
w15	0.11089	w110	−1.6209	−	−	−	−	
w25	−0.32486	w210	−0.92253	−	−	−	−	
w35	−3.272	w310	−1.2158	−	−	−	−	

The selected ANN model generated the output data set of predicted UAE lycopene yield with a low root mean square error (RMSE) of 5.3 mg/kg and a high correlation coefficient (R) of 0.99914. The analysis of the obtained results based on the ANN model outputs regarding the investigated ultrasound-assisted extraction system:enzymatically treated experimental matrices-extraction solvent.

To design an ANN architecture that can be reevaluated by generating reproducible results, the input data matrix was divided into training (70 %), validation (15 %), and test (15 %) sets. The completion of the training procedure using the LM algorithm generated the desirable feedforward backpropagation ANN. The error histogram of the trained feedforward ANN presented in Fig. 8 leads to the conclusion that the error margin of the target and predicted values verify the adequate design of the ANN and its architecture. The error values, considering the actual numerical values for the studied output, lycopene yield, are negligible and add to the conclusion derived from the detailed interpretation of the ANN predictive model results for the entire data set.Fig. 8 ANN model error histogram.

Interpretation of the regression plot (Fig. 9) suggests that the created ANN model produces a high correlation of output data and experimentally obtained values for the studied UAE system response yield of lycopene from enzymatically treated tomato peel matrices. The resulting correlation for each of the separate data sets-training, validation, and test as well as for the entire data set (R=0.99914) confirms the appropriateness of the designed neural network.Fig. 9 Regression plot for the designed UAE system.

The graphical presentation of the comparative analysis plot (Fig. 10) that represents the data series of ANN modeled outputs and experimentally observed values for the lycopene yield confirms the conclusion that the ANN model produces a high correlation factor for the entire input variables set. The interpretation of the discussed results suggests that experimentally observed values (series 1) are well-fitted by the ANN output values (series 2).Fig. 10 Comparative analysis of the appropriateness of fit for the experimental data and ANN model outputs for the lycopene yield.

4 Conclusions

The experimental plan completed in the frames of this research can be formulated in several engineering conclusions related to the design of the closed system − UAE of lycopene from enzymatically treated samples of tomato skins. The investigated raw material – tomato skins (bio-waste from the tomato processing industry) can be considered a potential secondary raw material for the isolation of lycopene thus fitting in the frames of sustainable circular technologies and basic principles of green process engineering and reducing the eco-footprint of the conventional process industry. The designed system for separation of the highly sought-after bioactive component − lycopene which also represents a very potent antioxidant, was based in UAE with several advantages over the conventional version of the process − reduced extraction time, energy consumption, and solvent waste. Furthermore, adequate enzyme preparations were applied to the described extraction system with a significant positive influence on the recovery of lycopene demonstrated as improved overall process efficiency. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions detected as a local maximum in the graphical analysis of the process dynamics of UAE (extraction time of 15 min).

An artificial neural network model with a structural architecture of 3–10-1 related to input-hidden/operative-output neural layers was developed and trained for predictive modeling of the closed extraction system. Data matrices comprised of operating parameter values used as input neurons were selected as training, validation and test data sets (70 %, 15 % and 15 %, respectively) and experimentally obtained values for lycopene content were defined as output neural layer. The ANN predictive model based on the feedforward-backpropagation neural network trained using the LM algorithm was successfully designed using the MATLAB Neural Network Toolbox. Utilized ANN software based on the predefined architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914). The SHAP summary plot resulting from the KNN algorithm model suggests that extraction time is the most significant experimental feature influencing the model’s predictions. Furthermore, other experimental variables of the UAE process – operating temperature and enzymatic treatment time have a limited positive impact on the KNN regression model.

CRediT authorship contribution statement

Stefan Kuvendziev: Writing – original draft, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Kiril Lisichkov: Writing – review & editing, Supervision, Methodology. Mirko Marinkovski: Writing – review & editing, Visualization, Formal analysis, Conceptualization. Martin Stojchevski: Writing – review & editing, Software, Methodology, Investigation. Darko Dimitrovski: Writing – review & editing, Supervision, Methodology, Investigation. Viktor Andonovikj: Visualization, Software, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The present research is part of a project funded by the Ministry of Education and Science of North Macedonia (project financing decision number: 08-15590/5) and co-funded by Bionika Pharmaceuticals.
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