Introduction

The improper disposal of different solid waste fractions leads to various environmental impacts, starting from all types of pollution: air, water, and soil pollution to impacts on human health and ecosystems. Improvement of solid waste management is considered an environmental challenge and there is a special focus on reduction of consumption, cutting food waste, and turning waste into a resource at European and international levels. In the EU, at the level of 2014, 465 kg of municipal solid waste (from the 475 kg of waste generated) was treated in the following way: 28% was recycled and 28% landfilled, 27% incinerated and 16% composted (Eurostat 2016). The total amount of municipal waste generated in 2016 has increased to 480 kg per person and it was also increased the percentages of waste recycled at 30% and of waste composted at 17% and decreased the percentage of waste landfilled at 25%, while the percentage of incinerated waste remained the same as in 2014 (Eurostat 2018). According to Eurostat (2016) in Romania, 82% of solid waste generated in 2013 was landfilled placing the country far behind other countries from Europe. Organic waste represents approximately 50% from waste included in the composition of household waste in Romania (Ghinea et al. 2012). Food waste represents a significant percentage of organic waste generated in Romania (around 72–76 kg of food waste/capita) (Ghinea and Ghiuta 2018). According to the regulation (1999/31/EC on waste disposal), it is necessary reduction of dumped biodegradable waste (EC Council Directive 1999; Ghinea et al. 2016). Composting of solid waste can be successfully applied for organic waste removal, being the most used treatment method in Austria (32%), followed by the Netherlands (27%) and Belgium (21%) (Eurostat 2016). Before starting the composting process, it must be carefully established the recipe based on the materials which can be composted. There are three types of materials: nitrogen rich or green materials (bread, eggs shells, fruits and vegetables, pasta and rice cooked, tea bags, etc.), carbon rich or brown materials (brown paper bags, cereal boxes, coffee filters, wood chips, etc.), and not recommended materials for composting (cheese or any dairy products, meat, fish, fat, food scraps, bones, etc.). The materials must be established in order to compel the microorganisms to conduct a rapid and complete composting process. The aim of this paper is to present a recipe developed in order to be used first at laboratory level for food composting considering factors such as C/N ratio, moisture, particle size etc., and the fact that food waste must be blended with a high carbon material. In order to establish the most suitable recipe, various solid wastes were selected and the most important parameters (such as pH, electrical conductivity (EC), moisture, ash, N and C contents, C/N ratio, heavy metals) for different mixtures were determined.

Before starting the composting process, it is necessary to establish the adequate food waste recipe for this process, considering that the optimum starting conditions for obtaining compost are 20:1–35:1 for C/N ratio, a pH between 5.5 and 8.0, moisture content 45–65%, oxygen concentration > 10%, and particle size between 2.5 and 5.0 cm (Tompkins 2005). The selection of materials included in the recipe is very important in order to obtain quality compost as can be observed in studies presented and discussed further in the results and discussion section. The composting materials must be found in the composition of waste generated by population and suitable for composting (carbon and nitrogen contents, etc.). The physical and chemical characteristics of composting materials must be performed before starting the composting process itself. Different materials were selected by us in order to establish the suitable mixtures/substrates. In this study, fruit waste such as apple pomace and peel, banana, orange and kiwi peels and vegetable waste potatoes and carrot peels, and also cabbage leaf from the following reasons were selected:

Apple (Malus domestica)

It is cultivated in the entire world, about 30–40% of total world production is damaged and does not reach the market, while 20–40% is used for juice extraction. The remaining wastes after the extraction operation are called apple pomace and could be used in composting process (FAO 2013). Apples are produced in all EU states (12,568.5 thousand tonnes in 2016) where the main producers are Poland, Italy, and France. In Romania, 456.9 thousand tonnes of apples were produced in 2016 (Eurostat 2017).

Banana (Musa acuminata)

It is consumed in the entire world and the banana wastes include small-sized and damaged bananas and also banana peels. The composition of banana peels is 8% crude protein, 6.2% ether extract, 13.8% soluble sugars, 4.8% total phenolics, and others. These peels are rich in trace elements such as Fe, Cu, and Zn (FAO 2013). 6.1 million tonnes of banana were imported in the EU in 2016. The increase of banana imports is supported by strong consumption with an average of 12 kg per capita consumption per annum (EC 2017).

Orange (Citrus aurantium)

Global production in 2017/2018 is expected to be at 49.3 million metric tonnes (UESDA 2018). In EU, the orange production was 6.4 million tonnes in 2016 and majority of oranges were produced in Spain and Italy (Eurostat 2017). From orange consumption, orange peel, seeds, or orange pulp represents the residues. The orange pulp is the residue that remains after juice extraction and contains peel (60–65%), internal tissues (30–35%), and seeds (10%) (FAO 2013).

Kiwi fruits (Actinidia chinensis)

Kiwi fruits rich mainly in vitamin C and fibers, calcium, iron, and phosphorus have high nutritional value. The main producers of these fruits are Italy, China, New Zealand, and Chile according to Harder and Arthur (2012). In 2016, 598,559 metric tonnes of kiwi were produced in Italy (Bettini 2016).

Potato (Solanum tuberosum)

It is one of the major world crops after wheat (Triticum L.), rice (Oryza L.), and maize (Zea mays). Large amounts of potato peel waste (15 to 40%) are resulting after potato processing depending on the used method. Compounds like phenols, dietary fibers, unsaturated fatty acids, and amide are found in potato peel (Sepelev and Galoburda 2015). 55,969.8 thousand tonnes of potatoes were produced in 2016 in EU, where Germany and Poland are the main producers. In Romania in the same year, 2689.7 thousand tonnes of potatoes were obtained (Eurostat 2017).

Cabbage (Brassica oleraceae)

It is a common leafy vegetable rich in phenolics, vitamins, and minerals. Over 4.9 million tonnes of cabbage and other brassicas were produced in EU and 994,596 tonnes in Romania in 2016 according to FAO (2017).

Carrots (Daucus carota)

Carrots are root vegetables rich in carbohydrates and minerals (Sharma et al. 2012). In EU, 5.6 million tonnes of carrots were produced in 2016, the main producers are Poland and the UK. In the same year, 111.7 thousand tonnes of carrots were produced in Romania (Eurostat 2017). Carrot peel represents 11% of the amount of fresh carrot weight and has higher level of phenolics (Sharma et al. 2012).

From post-production, handling and storage, manufacturing, wholesale and retail, and consumption stages included in food supply chain are generated food waste. According to worldwide from food supply chain, an amount of 70–140 t/year of potato peel, 3,000,000–4,200,000 t/year of apple pomace are generated. Also, an average of 700 t/year of orange peel is produced in USA (Ravindran and Jaiswal 2016).

Materials and methods

All analyses were performed at Stefan cel Mare University of Suceava. Fruits and vegetable waste were collected from household waste: first they were selected and then separately chopped to pieces of 2–3 mm (Fig. 1), weighed in the laboratory, and placed in plastic airtight containers at constant temperature (approximately 4 °C) until the experiments, excepting the samples used for moisture determination. This analysis was performed immediately. The varieties of studied fruits and vegetables collected were Malus domestica Borkh., Musa acuminata—Dwarf Cavendish banana, Citrus aurantium var. sinensis, Actinidia chinensis var. deliciosa, Solanum tuberosum var. tuberosum, Brassica oleraceae var. capitata f. alba, and Daucus carota var. sativus.

Fig. 1
figure 1

Steps performed

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In order to establish the main properties of food waste, the samples were characterized and the most important parameters (pH, EC, moisture (M), ash (A), N and C contents, C/N ratio, heavy metals) were identified. Three replicates were performed in order to determine the parameters mentioned above. Each type of fruits and vegetable waste samples was homogenized before determining these parameters.

The samples were prepared for determination of pH and EC based on the method used by Nasreen and Qazi (2012): 1 g of each type of fruits and vegetables waste sample was mixed with 10 mL of distilled water, for 1 h was shaken at 150 rpm, and after that for 10 min, the samples were centrifuged at 10,000 rpm. For each filtered sample, the pH and EC values with IQ240 pH Meter and CyberScan CON 510 Conductometer were obtained.

Determination of pH and EC is performed in most of the studies in which composting is being analyzed (Azim et al. 2014; Dumitrescu et al. 2009; Kulcu 2014; Nasreen and Qazi 2012; Sharma et al. 2018) since they are parameters that influence the growth of bacteria and fungi. The variations of EC are depending on the amount of mineral salts dissolved in suspension. EC indicates the quality of compost used as fertilizer since it is associated with compounds that are easily released into solution (Sharma et al. 2018; Zaha et al. 2013). The salt content in the compost can be a limiting factor for various types of crop grown. For EC determination, the same condition for sample preparation as for pH was used.

Moisture was determined by oven-drying method. The samples were prepared as follows: 5 g of each homogenized fruits and vegetable waste was weighed and dried at 105 °C for 24 h until constant weight (Villar et al. 2016

$$ \% Moisture={\frac{W_{ws}-{W}_{ds}}{W_{ws}}}^{\ast }100 $$
(1)

where Wws—wet weight of the sample (g) and Wds—sample weight after drying.

Moisture is another important parameter that influences the microbiological activity and represents the medium for dissolved nutrient transportation. When moisture is in excess affects the oxygen transport since it will fill the pores between particles (Muter et al. 2014).

Five grams of food waste samples was weighed, homogenized, and combusted at 550 °C for 4 h for determination of ash content (Villar et al. 2016). An oven made by Nabertherm was used. The ash content was used for calculation of volatile solids which further they were considered for the determination of carbon.

Carbon content (C %) was estimated according to Adams et al. (1951) method (Eqs. 2–3):

$$ \% VS=100-\% Ash $$
(2)
$$ \%C=\left(\% VS\right)/1.8 $$
(3)

where VS represents the volatile solids.

This equation was also used by Barrena et al. (2007), Barrena et al. (2009), Ferreira et al. (2012), Feyssa et al. (2015), Valero et al. (2016), and Rawaengsungnoen et al. (2018).

The Kjeldahl method applied for nitrogen determination has three basic steps: (1) digestion of the sample in sulfuric acid with the catalyst, resulting the conversion of nitrogen to ammonia; (2) distillation of the ammonia into a trapping solution; and (3) quantification of the ammonia by titration with a standard solution. The samples were dried until their weights were stable at 105 °C (Yanu and Jakmunee 2017). 0.2–1 g of each homogenized sample dried was introduced in Kjeldahl flask with 20 mL of H2SO4 concentrated, 0.5–1.0 g of CuSO4, and 2.0–5.0 g of K2SO4. The Kjeldahl flasks are introduced for digestion: if in the first stage the liquid from flasks is black, in the second phase, the liquid is clear. After digestion (which takes place in 4–5 h), distillation (20–25 min) and nitrogen dosing were performed. For the nitrogen determination, DK Series Kjeldahl Digestion Units-VELP Scientifica was used.

The classical Kjeldahl method is officially recognized by AOAC, EPA, DIN, and ISO and at European level, there are standards like EN 13654-1 (2002), TC (2005), EN 16169 (2012), etc. which are taken into consideration. Kjeldahl method was used for total nitrogen determination in various studies by different authors such as Sobiecka et al. (2007) performed a validation study for determination of Kjeldahl nitrogen in soils, sludge, and treated biowaste for the development and validation of the European standard, the co-composting of hair waste was studied by Barrena et al. (2007); Barrena et al. (2009) used animal by-products for composting at laboratory level; Ferreira et al. (2012) analyzed the co-digestion of wasted sardine oil with pig slurry; Wang et al. (2016) investigated the possibility of the compost quality improvement during the pig manure composting; Chan et al. (2016) analyzed the reduction of nitrogen loss and salinity during food waste composting; Epelde et al. (2018) have characterized various composted organic amendments and proposed a methodology in order to select the suitable amendments; Saez-Plaza et al. (2013) in their paper presented application of Kjeldahl methods in different studies with various samples among them food products and also waste; Simonne et al. (1997) suggested that Dumas method could replace Kjeldahl in food analysis in some cases when the differences are small while in other cases are necessary adjustment and concluded that for fruit and vegetable groups are needed adjustment; and Mihaljev et al. (2015) consider that Kjeldahl method is still the most commonly used and well-known method for nitrogen determination due to high precision and very low variation interval, but instead is not as fast as the Dumas method and uses concentrated sulfuric acid at high temperatures.

Knowing that the quantity of heavy metals in the compost depends on the quality of the input materials, in this study, the heavy metal content from the fruits and vegetable waste was determined as follows: 3 g of each fruits and vegetable waste sample was calcined for 5 h at 600 °C. After that, in a 25-mL volumetric flasks over the obtained ash were added 0.362 mL HNO3 69% and it was filled up to the mark with deionized water. Heavy metal content was determined with a mass spectrometer with inductively coupled plasma mass spectrometry (ICP-MS) Agilent Technologies 7500 Series (Prisacaru et al. 2017).

The obtained results were investigated further by applying one-way ANOVA (using Minitab software, version 17). The p value was calculated and compared with the value of α-level (0.05) in order to establish if the differences between group means are statistically significant. Also, with Minitab software, the regression equations which can be used for calculation of each type of waste amount were determined.

Results and discussion

The optimum values of pH for composting are between 5.5 and 8.0. The results obtained by us are illustrated in Fig. 2a, it can be observed that the pH of fruit waste is acid for oranges and kiwi waste the values are below 4.0, for apples and banana waste the values are around 5.0, while for vegetable waste (potatoes, carrots and cabbage) the pH is between 6.0 and 6.5, for carrots waste being recorded a lower value (Fig. 2a). Nasreen and Qazi (2012) obtained a pH value of 3.36 for apple and 6.53 for banana peels, in the case of orange peels, the pH is 3.23, while for potato peels, the pH value is 6.25. These values are similar to ours except for the pH value for apple peels. There is a close relationship between pH and odor emission. Sundberg et al. (2013) analyzed the odor emission from two composting plants and concluded that at high values of pH (above 6.5), it was observed low odor, while high odor was registered at pH below 6.0. During the composting process, the pH values will increase due to decomposition of organic matter by microorganisms. Even so we have to carefully choose the quantities of waste to form the composting mix. We will consider a larger amount of vegetable waste (with pH around 6.0) and lower amounts of fruits waste such as orange and kiwi waste.

Fig. 2
figure 2

Physico-chemical analysis. a pH. b Electrical conductivity. c Moisture. d Ash. e Nitrogen content

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Results showed that cabbage, carrots, and apple waste have EC low values (between 100 and 270 μS/cm), while for kiwi, banana, and potato waste exceed 1000 μS/cm as it can be observed from Fig. 2b.

The values obtained for moisture (Fig. 2c) indicated that all types of waste investigated in this paper have very high moisture content with values around 90% for bananas, carrots, and cabbage, while for apples, kiwi, and potatoes, the values are between 80 and 82%. Only for orange waste, the moisture content is lower compared to the other types of waste investigated (around 65%). In the case of Nasreen and Qazi (2012) study, the moisture content for apples was 72.33%, 91.33% for bananas, 76.33% for oranges, and 82.00% for potatoes. These values are quite similar with our values.

Determination of ash content was performed for all types of waste considered for analysis and it was found that the potatoes, banana, and orange waste have ash content between 7.0 and 9.0% (the highest value was recorded for potatoes waste), while for the others, the values are around 4.0% for cabbage and 5.4% for kiwi waste (Fig. 2d).

Nitrogen content was investigated and the results showed that N content is below 1% with values between 0.18% for potatoes waste and 0.35% for carrot waste (Fig. 2e). The nitrogen content decreases in the following order: carrots > cabbage > apples > banana > kiwi > orange > potatoes.

Chromium concentrations range between 2.5 and 15.4 mg/kg (mean values), with the highest concentration detected in a sample of banana waste (15.4 mg/kg), in vegetable waste the highest value of Cr was registered for cabbage (7.441; 8.026 mg/kg) (Fig. 3a). The Cr concentration in the investigated fruits and vegetable samples exceed the maximum permissible limit value of 2.3 mg/kg according to Codex (2001). Maleki and Zarasvand (2008) reported a value of 7.9 mg/kg Cr in vegetable samples.

Fig. 3
figure 3

Heavy metal contents determined in fruit and vegetable wastes. a Cr. b Co. c Ni. d Cu

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The highest concentration of Co was observed in the case of banana waste (0.50 mg/kg), followed by potato waste (0.43 mg/kg) (Fig. 3b), while the lowest level was obtained for orange waste (0.02 mg/kg).

In this study, the amount of Ni in the tested samples varied from 0.6 to 3.5 mg/kg (Fig. 3c). The maximum accumulation of Ni was found in carrot waste (2.529; 4.471 mg/kg) followed by banana waste (2.449; 4.391 mg/kg). WHO/FAO established the maximum permissible value at 10 mg/kg for Ni concentration in vegetable sample (Codex 2001). From Fig. 3c, it can be very well observed that Ni values are below the maximum admissible concentrations. Shaheen et al. (2016) reported a level of Ni in banana of 0.037 mg/kg, 0.103 mg/kg in carrot (values much lower than ours), and a similar value to ours for the Ni level in potatoes (0.643 mg/kg).

From Fig. 3d, it can be observed that the highest concentration of Cu was obtained for the banana waste samples 11.5 mg/kg (mean value), while the lowest Cu level was registered for the apple waste (1.7 mg/kg—value within the limits of those obtained by Roba et al. (2016) 0.9–3.6 mg/kg). The safe limit, which is 40 mg/kg according to WHO/FAO for the vegetable samples (Codex 2001), was not exceeded in this case. A level of 0.946 mg/kg Cu was reported by Shaheen et al. (2016) for banana, 2.254 mg/kg for carrot, and 3.632 mg/kg for potato. In our case, the values registered were around 2 mg/kg of Cu in carrots and potato waste. For kiwi, we have obtained a value of 2.4 mg/kg which is higher than the value (1 mg/kg) reported by Grembecka and Szefer (2013), also for orange the level of Cu registered by us was 2.1 mg/kg, while Grembecka and Szefer (2013) obtained 0.3 mg/kg.

The Cd content among fruits and vegetable waste varies between 0.0014 mg/kg for apple waste and 0.032 mg/kg for banana waste. According to the EC 1881/2006, the maximum levels of Cd are 0.05 mg/kg wet weight (for fruit), 0.10 mg/kg wet weight (for root vegetables and potatoes), and 0.20 mg/kg wet weight (for leaf vegetables). Our results showed that the Cd content in fruit and vegetable waste is lower than the maximum levels. The concentration of Cd in potato waste determined by us was 0.027 mg/kg slightly higher than that obtained by Shaheen et al. (2016) (0.013 mg/kg), while for carrots, we have obtained a lower level of 0.006 mg/kg compared with 0.023 mg/kg reported by Shaheen et al. (2016). For cabbage, we have obtained a value of 0.02 mg/kg, while Stančić et al. (2016) reported 0.20 mg Cd/kg.

The zinc concentration in the investigated waste samples decreased in the following order: 2.63 mg/kg in banana waste > 0.73 mg/kg in cabbage waste > 0.35 mg/kg in kiwi waste > 0.32 mg/kg in orange waste > 0.313 mg/kg in potato waste > 0.31 mg/kg in apple waste > 0.23 mg/kg in carrot waste. The values obtained by us for the level of Zn in vegetables are much lower than those reported by Roba et al. (2016) for the vegetables cultivated in the mining area (8.2 mg/kg for potato, 21.7 mg/kg for carrot, and 4.9 mg/kg for cabbage). In the literature, Zn concentration reported for banana was 0.235 mg/kg (Shaheen et al. 2016), 5.59 mg/kg (Radwan and Salama 2006), and 1.5 mg/kg (fruits from Costa Rica) and 1.8 mg/kg (fruits from Ecuador) (Grembecka and Szefer 2013).

The heavy metal concentrations are unaffected during the organic degradation and utilization of compost obtained from organic waste can contribute to heavy metal bioaccumulation in plants and may be transferred into the food chain (Mohee and Soobhany 2014). Considering these and the fact that EU regulation on heavy metals limits for compost are 70 mg/kg for Cr, 70 mg/kg for Cu, 0.7 mg/kg for Cd, 25 mg/kg for Ni, and 200 mg/kg for Zn (EC 2014), it can be stated that the heavy metal content of the compost which will be obtained from this food waste may not exceed the admissible limits. This aspect can be clearly seen when the compost is actually obtained, and will be presented and discussed in future studies. The only issue that can be encountered is the cobalt content that according to the regulation should not be found in the compost.

In order to establish the suitable amount of fruit and vegetable waste, considering the values of moisture (M), carbon (C) and nitrogen (N) contents, and C/N ratio, the regression analysis has been used. In the first phase, the total amount of food waste (Qw = 5 kg) that is intended to use for obtaining of compost at laboratory level was established. Also, the total amounts of fruit waste (Qfw) and vegetable waste (Qvw) were determined (Table 1) after calculation of C/N ratios. There were chosen four different C/N ratios: 25:1, 30:1, 35:1, and 40:1.

Table 1 Necessary amounts of fruits and vegetable waste for different C/N ratios
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In the second phase, the parameter settings for regression analysis were considered (Ghinea et al. 2016): confidence interval—95; type of confidence interval—two-sided; sum of squares for tests—adjusted (type III); λ (for Box-Cox transformation) = 0.5 (square root). Calculation of apple waste amount (Qaw), banana waste amount (Qbw), orange waste amount (Qow), kiwi waste amount (Qkw), potato waste amount (Qpw), carrot waste amount (Qcw), and cabbage waste amount (Qcw) which are included in sample 1 (S1) can be performed by using Eq. (4). For the other samples, the following equations can be applied: for S2 (Eq. 5), for S3 (Eq. 6), and for S4 (Eq. 7). The regression lines describe the relationship between the response and predictor variables (Ghinea et al. 2016).

$$ {Q_1}^{\hat{\mkern6mu} 0.5}=-3.78+0.046{M}_1+0.000526{C}_1+0.1233{N}_1+0.01047{C}_1/{N}_1 $$
(4)

where Q1—amount of food waste in sample 1; M1—moisture content in sample 1; C1—carbon content in sample 1; N1—nitrogen content in sample 1; C1/N1—ratio in sample 1.

$$ {Q_2}^{\hat{\mkern6mu} 0.5}=-3.595+0.0431{M}_2-0.00015{C}_2+0.1493{N}_2+0.01202{C}_2/{N}_2 $$
(5)

where Q2—amount of food waste in sample 2; M2—moisture content in sample 2; C2—carbon content in sample 2; N2—nitrogen content in sample 2; C2/N2—ratio in sample 2.

$$ {Q_3}^{\hat{\mkern6mu} 0.5}=-4.45+0.0525{M}_3-0.0003{C}_3+0.138{N}_3+0.0153{C}_3/{N}_3 $$
(6)

where Q3—amount of food waste in sample 3; M3—moisture content in sample 3; C3—carbon content in sample 3; N3—nitrogen content in sample 3; C3/N3—ratio in sample 3.

$$ {Q_4}^{\hat{\mkern6mu} 0.5}=-0.513+0.01303{M}_4-0.000182{C}_4+0.05375{N}_4+0.003581{C}_4/{N}_4 $$
(7)

where Q4—amount of food waste in sample 4; M4—moisture content in sample 4; C4—carbon content in sample 4; N4—nitrogen content in sample 4; C4/N4—ratio in sample 4.

In order to find out how the model fits, the data values of S, R-sq, and R-sq(adj) were investigated. The meaning of these indicators is explained by Ghinea et al. (2016): S represents the standard distance that data values fall from the regression line, R-sq or R2 describes the amount of variation in the observed response values that is explained by the predictors, while adjusted R-sq(adj) is a modified R2 that has been adjusted for the number of terms in the model. In this case, S has lower values and R-sq values are close to 100 which mean that the outcomes are better. For the first model, the following values were obtained: S = 0.053, R-sq = 99.47, and R-sq(adj) = 98.41%. In Table 2, the amounts of fruits and vegetable waste for different C/N ratios are presented.

Table 2 Amounts of fruits and vegetable waste necessary to obtain different C/N ratios
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The pH values of fruits and vegetable waste mixture are between 5.35 (for S4) and 5.90 (for S1). Low pH phase can be overcome by high aeration rates and by adding of additives like recycled compost (Sundberg et al. 2013).

Knowing that in this phase the moisture content of fruits and vegetable waste mixture is too high 87.72% for S1, 86.35% for S2, 84.53% for S3, and 83.01% for S4 (optimum is between 45.00 and 65.00%), it was considered to add to each sample a determined amount of sawdust in order to obtain lower values of moisture content, to decrease the air voids between the waste materials, and to increase the carbon content of the sample. Also, the sawdust leads to an increase in pH during composting acting as an inhibitor of bacterial activity and has a critical role for maintaining the aerobic condition and for leachate retention (Sharma et al. 2018; Zaha et al. 2013). In this purpose, the following Eq. (8) was used

$$ {x}_2={\frac{\%{N}_{x1}}{\%{N}_{x2}}}^{\ast }{\frac{R-{R}_{x1}}{R_{x2}-R}}^{\ast}\frac{1-{M}_{x1}}{1-{M}_{x2}} $$
(8)

where x1—amount of food waste mixture; x2—amount of sawdust; Nx1—% nitrogen in food waste mixture; Nx2—% nitrogen in sawdust; Mx1—% moisture in food waste mixture; Mx2—% moisture in sawdust; Rx1—C/N ratio of food waste mixture; Rx2—C/N ratio of sawdust; R—desired C/N ratio.

For calculation, the following values were used: 20% moisture content of sawdust, 0.1% nitrogen in sawdust, C/N ratio = 475. In this way, the necessary amounts of sawdust for each sample were calculated and the moisture content of considered samples dropped to 45%.

The regression analyses were performed with Minitab 17 for all samples (S1–S4 which has included in this stage also the values of indicators for sawdust besides the indicators values for food waste) considering as outputs the values of C/N and as inputs were introduced the values of Q, M, C, and N. The results obtained were regression equations for C/N ratios, analysis of variance for transformed response, model summary for transformed response, and residual plots. Table 3 presents the values obtained for the following indicators S, R-sq (%), R-sq(adj) (%), and R-sq(pred)(%) after the analysis of each sample. The lowest S value was obtained for sample 4 and the highest value for sample 3, while the values of S for samples 1 and 2 are very close. R-sq and R-sq(adj) values for each sample exceed the 99%, some of them being very close to 100 (for example, the values registered for sample 4). Regarding R-sq(pred), the highest value was obtained for sample 1, while for sample 3, the R-sq(pred) is 0. The outcomes are much better in the case of sample 1 since the values of all R-sq are much closer to 100 than the R-sq values obtained for the other samples. In Table 4, the variation amount in data response (for sample 1) explained by Q, M, C, and N predictors is presented. α-level is used for interpretation of p value and in this case is 0.05. p value is 0.000 for M, which means that at least one of the regression coefficients is significantly different than zero (Ghinea et al. 2016), and different than 0.000 for Q, C, and N but higher than the value of α-level. The same situation was observed also after investigation of the other three samples with the mention that in the case of sample 3, the p values for Q and N are lower than the value of α-level. These mean that M is the most significant factor for the analysis. Variance inflation factor (VIF) values are useful to view how much the predictors are correlated. In the case of sample 1, M and C predictors are moderately correlated (1 < VIF < 5), while for Q and N are highly correlated (VIF > 10) (Table 5). The same situation was observed in the case of sample 2, while for sample 3, all predictors are moderately correlated and for sample 4, Q, M, and N predictors are highly correlated.

Table 3 Model summary for transformed response
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Table 4 Analysis of variance for transformed response—sample 1 (S1)
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Table 5 Coefficients for transformed response—sample 1 (S1)
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Figure 4 illustrates the residual plots for C/N of sample 1. The points from the normal probability plot are close to the line which means that for the evaluated data, the normal distribution represents a good model (Ghinea et al. 2016). The variability is between − 0.6 and 0.4 (histogram) with a possibility of one outlier existence. Grubbs’ test was performed in order to find out that is there one outlier or not. Results suggested that the mean of sample is 90.3 and the G statistic indicates that the higher data value, 475, is 2.45 standard deviations higher than the mean. This p value is less than significance level (0.05) and the null hypothesis (all data values come from the same normal population) can be rejected.

Fig. 4
figure 4

Residual plots for C/N—sample 1

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Regression equations obtained for each samples are represented by Eqs. (9–12):

$$ {C}_1/{N_1}^{\hat{\mkern6mu} 0.5}=25.877+2.93{Q}_1-0.2477{M}_1+0.01140{C}_1-0.963{N}_1 $$
(9)

where Q1—amount of food waste in sample 1; M1—moisture content in sample 1; C1—carbon content in sample 1; N1—nitrogen content in sample 1; C1/N1—ratio in sample 1.

$$ {C}_2/{N_2}^{\hat{\mkern6mu} 0.5}=25.860+3.49{Q}_2-0.2423{M}_2+0.00559{C}_2-1.112{N}_2 $$
(10)

where Q2—amount of food waste in sample 2; M2—moisture content in sample 2; C2—carbon content in sample 2; N2—nitrogen content in sample 2; C2/N2—ratio in sample 2.

$$ {C}_3/{N_3}^{\hat{\mkern6mu} 0.5}=26.543+2.53{Q}_3-0.2541{M}_3+0.00162{C}_3-0.581{N}_3 $$
(11)

where Q3—amount of food waste in sample 3; M3—moisture content in sample 3; C3—carbon content in sample 3; N3—nitrogen content in sample 3; C3/N3—ratio in sample 3.

$$ {C}_4/{N_4}^{\hat{\mkern6mu} 0.5}=29.172+18.31{Q}_4-0.386{M}_4-0.00169{C}_4-1.338{N}_4 $$
(12)

where Q4—amount of food waste in sample 4; M4—moisture content in sample 4; C4—carbon content in sample 4; N4—nitrogen content in sample 4; C4/N4—ratio in sample 4.

The surface plots of C/N ratio versus the four indicators evaluated are illustrated in Fig. 5. It is considered that the highest number of defects occur when Q values are high and M values lower (Fig. 5a). The response surfaces are with no curvature and the surface plots from Fig. 5c and Fig. 5e look similar with this one. The highest values of C/N are in the upper right corner which corresponds to high values of both Q and C (Fig. 5b). In order to calculate the fitted response values of C/N in this case, Minitab holds the value of M and N constant at 75.17 and 2.2, respectively. From Fig. 5d, it can be observed that the highest values of C/N are registered in the upper left corner when M values are lower and C values higher. Figure 5f shows that the lower values for both N and M correspond to high values of C/N.

Fig. 5
figure 5

Surface plots of C/N vs a M, Q; b C, Q; c N, Q; d C, M; e N, C; f N, M

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Melon waste, tomato waste, sheep manure, and olive mill waste were mixed by Azim et al. (2014) in different proportions for compost production. These types of waste were chosen based on their availability and carbon/nitrogen content. The physical and chemical characteristics (total carbon, total nitrogen, C/N ratio, and moisture content) of these four types of waste were also determined by Azim et al. (2014). The proportion of each waste fraction used is illustrated in Fig. 6a. The composting was performed in pile; the authors monitored temperature, moisture, pH, C/N ratio, P, K, Ca, Mg, humic, and fulvic acid content. Azim et al. (2014) evaluated the quality of the compost using three methods: germination, cress test, and green bean growth. Their results showed that the highest germination percentage had samples with C/N ratio equal with 30 and 35.

Fig. 6
figure 6

Proportion of waste types used by a Azim et al. (2014); b El Zein et al. (2015); c Muter et al. (2014) and C/N ratios; d Kulcu (2014); e Zaha et al. (2013); and f this study

Full size image

El Zein et al. (2015) used for co-composting four waste ingredients: municipal solid waste, fishery wastes, banana plantation wastes, and anaerobically composted slaughterhouse (composted meat). These wastes were also chosen based on their availability and were left for decomposition in a mechanical barrel and after 22–24 days were pilled up in open ground space. Temperature and moisture were monitored by El Zein et al. (2015) and they also measured pH, C/N, total carbon, total nitrogen, etc. during composting process. They performed three different experiments: the first one involved two mix combinations of the four types of waste and was performed in a mechanical rotating barrel (the experiment failed because of some mechanical problems); in the second experiment, they combined banana waste (BW), fish waste (FW), composted meat (CM), and MSW in the following proportions 4:1:1:1 (barrels 1 and 2; C/N = 18.97–22.64), 1:1:1:1 (barrel 3; C/N = 13.8–16.5) while in the last experiment performed in three trial steps considering 27, 45, and 91 days for composting process, the proportion used and C/N ratios are illustrated in Fig. 6b. The results obtained by El Zein et al. (2015) show that banana weight is the waste which influence the most C/N ratio and fish waste can be successfully used to obtain a compost with good maturity.

Nasreen and Qazi (2012) performed composting process at laboratory scale using apple, banana, orange, and potato peels as raw materials. They measured pH, EC, ash, moisture, seed germination, and bacteria during 3 weeks. The waste fractions were not mixed; each fraction (about 120 g) was composted separately in jars. Nasreen and Qazi (2012) proved that composting of fruits and vegetables using forced aeration at 50 °C can be accomplished in only 3 weeks.

Muter et al. (2014) mixed organic potato pulp with grass different combinations (75:25, 50:50, and 25:75% w/w) in order to obtain compost in windrows. They analyzed concentration of ash, C, N, K, P, and S of raw materials and composted substrate and also calculated the C/N ratio (Fig. 6c). Muter et al. (2014) observed that increasing the amount of grass influences the composting process leading to an increase of temperature in windrows. Muter et al. (2014) demonstrated that potato pulp in combination with grass is a suitable substrate for composting, C/N ratio it is within limits (25–30) and concluded that potato pulp: grass in ratio 1:1 is considered the suitable solution in the conditions studied by them.

de Campos et al. (2014) studied the composting of organic household waste (146 L) and pine sawdust (29 L) in a reactor and performed monitoring of temperature, moisture, pH, ash content and C/N ratio, spectroscopic analyses, and germination index.

Tomato plant residues, wheat straw, and dairy manures were used by Kulcu (2014) as raw materials for composting at laboratory level in five bioreactors. The physical and chemical analysis (C/N, N, P, K, electric conductivity, pH, organic matter, moisture content, free air space) of these three types of waste was performed by Kulcu (2014). The waste mixture and C/N ratio are illustrated in Fig. 6d. Kulcu (2014) observed that the mixture of tomato residue (60%), wheat straw (10%), and dairy manures (30%) was more suitable than the other four mixtures for composting. Zaha et al. (2013) used the following materials for composting: vegetable waste, beech sawdust, and sewage sludge in three different mixtures (Fig. 6e). They monitored the most important composting parameters, investigated biodegradation of organic matter using FTIR analysis, and preformed germination tests. Their results showed that all sample composition were suitable for composting.

In this study, from Fig. 6f, it can be observed that vegetable wastes, especially carrots, are the main ones that influence the C/N ratio, between the limits 25–30 provided by the literature as being most favorable to the composting process. Orange peel wastes are the component which significantly contribute to C/N ratio = 40, followed by kiwi, apple, and banana wastes, while apple waste is the main contributor in order to obtain the C/N ratio value of 35.

Other types of food waste and materials used for obtaining compost were wood shavings, tomato plant residues, municipal solid waste and urea used as nitrogen source (moisture content 90% and C/N = 30:1) (Ghaly et al. 2006); vegetable waste (potatoes, carrots, cabbage, apples, and banana), sewage sludge, sawdust in different proportions (Dumitrescu et al. 2009), vegetable waste (cauliflower, cabbage, beans, potato, pumpkin, spinach, coriander, capsicum, radish, carrot, cucumber, peas, etc.) and three leaves and grass cutting (Katre 2012).

Conclusions

In this study, we determined the physico-chemical parameters of food waste and applied the regression analysis in order to develop a model for food waste composting. The following conclusions can be drawn:

  • the regression analysis can be successfully used for determination of equations which can be applied for calculation of the fruits and vegetable waste amounts necessary to obtain different C/N ratios;

  • the determined moisture content of fruits and vegetables was too high and for this reason, we decided to use sawdust, and based on one equation, the sawdust amount necessary to be added in each sample was calculated;

  • moisture is the most significant factor for the analysis. Also, we observed that moisture and carbon content predictors are moderately correlated, while the quantity of food waste and nitrogen content are highly correlated;

  • the highest values of C/N were obtained when moisture values are lower and carbon values higher;

  • in order to obtain C/N ratios between 25 and 30, the waste that must be used in greater quantity are carrots, followed by other types of waste, while apple wastes are the largest contributor for C/N ratio of 35.

In the future studies, we will investigate whether it is appropriate to use a C/N ratio of less than 25:1 and how this ratio will influence the composting process.