Commercial short-cut extruded pasta: Cooking quality and carbon footprint vs. water-to-pasta ratio

Commercial short-cut extruded pasta: Cooking quality and carbon footprint vs. water-to-pasta ratio

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159 Contents lists available at ScienceDirect Food and Bioproducts Processing journal homepage...

1MB Sizes 1 Downloads 96 Views

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Contents lists available at ScienceDirect

Food and Bioproducts Processing journal homepage: www.elsevier.com/locate/fbp

Commercial short-cut extruded pasta: Cooking quality and carbon footprint vs. water-to-pasta ratio Alessio Cimini a , Matteo Cibelli a , Maria Cristina Messia b , Mauro Moresi a,∗ a

Department for Innovation in the Biological, Agrofood and Forestry Systems, University of Tuscia, Via S. C. de Lellis, Viterbo 01100, Italy b Department of Agricultural, Environmental and Food Sciences, University of Molise, Via F. De Sanctis, Campobasso 86100, Italy

a r t i c l e

i n f o

a b s t r a c t

Article history:

Pasta cooking is an energy-intensive process. Its energy requirements might be significantly

Received 15 March 2019

cut by reducing simultaneously the cooking water-to-dry pasta ratio (WPR) and effective

Received in revised form 18 April

power supplied during the so-called pasta cooking phase. In the case of cooked short-cut

2019

extruded pasta of the Penne rigate type, the textural (i.e., hardness at 40% and 98% compres-

Accepted 15 May 2019

sion, and resilience) and chemical (i.e., relative water uptake, degree of starch gelatinisation,

Available online 22 May 2019

and cooking loss) parameters were almost constant for WPR ranging from 3 to 10 L kg−1 .

Keywords:

needs reduced by about two-thirds with respect to the conventional WPR value of 10 L kg−1 .

When cooking one kg of short pasta with just 3 L of water under mild mixing the energy Carbon footprint

In this way, it would be possible to reduce the greenhouse gases emitted to sustain the

Cooking energy consumption

current consumption of dry pasta by about 50%.

Minimum and effective cooking

An empirical equation was also developed to predict a minimum WPR value assuring no

water-to-dried pasta ratio

agglomeration of any pasta type (short or long) by accounting for the external surface and

Penne rigate

empty volume of each pasta piece, and most probable water uptake by cooked pasta.

Cooking quality

© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Texture analysis

1.

Introduction

Dry pasta is a staple food largely produced and consumed worldwide. Its overall production is of approximately 14.3 million metric tons, the 22.7% and 14.0% of which being produced in Italy and USA, respectively (UNAFPA, 2015a). The cradle-to-grave carbon footprint (CFCG ) of dried pasta was found to range from 1.93 to 3.03 kg carbon dioxide equivalents (CO2e ) per kg of dry pasta, depending on the use of a gas or electric stove, respectively (Barilla, 2017). Thus, the contribution of pasta cooking (CFPC ), equalling to 0.6 or 1.7 kg CO2e kg−1 , respectively, might represent from the 31 to 56% of CFCG . Since the per capita consumption of pasta in Italy is about 23.5 kg yr−1 (UNAFPA, 2015b) and current Italian population is 59,245,662 (Worldometers, 2019), the greenhouse gas (GHG) emissions associated with the Italian consump-

tion of dry pasta would range from 2.69 to 4.22 Tg CO2 yr−1 . Such emissions would represent as much as the 0.7 or 1.1% of the overall Italian GHG emissions in 2016 (e.g., circa 398 Tg CO2e when including those adsorbed by land use, land use change and forestry: ISPRA, 2018). Thus, any attempt to improve the sustainability of dry pasta should be firstly aimed at cutting the GHG emissions associated with its cooking. Dry pasta is generally cooked in boiling water, such a temperature being required to enhance convective motions within the water-pasta suspension and thus increase heat and mass transfer from the bulk to the pasta surface throughout the cooking phase (Piazza et al., 1994; Sicignano et al., 2015). Such motions, as well as pasta mixing, are essential to cook homogeneously pasta with no agglomerates and/or partly cooked areas. The pasta cooking energy needs are not only dependent on the home appliance (i.e., gas-fired or electric stoves), but also on the cooking water-to-pasta ratio (WPR) used. The overall energy efficiency (␩C )



of dried pasta cooking using gas-fired and induction hobs was found to

Corresponding author. vary from 30 to 46%, respectively (Cimini and Moresi, 2017). The inducE-mail address: [email protected] (M. Moresi). https://doi.org/10.1016/j.fbp.2019.05.004 0960-3085/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Nomenclature AC AD bR CFCG CFPC CL DW di dRb dRu EPC ES ERC

eCE

ePA F40 F98 Foh Fom GHG GWPEE

HW0 HPA HPLS HWeb HCPb HPLSb, HWFb hR iR Lp mCP mPA mPLS mp mWE mWF mWPA mW0 np

Downstroke energy to compress the pasta pieces by 40% of their initial thickness [J] Upstroke energy to decompress the 40%compressed cooked pasta pieces [J] Rib base [mm] Cradle-to-grave carbon footprint of dry pasta [kg CO2e kg−1 ] Carbon footprint of pasta cooking [kg CO2e kg−1 ] Cooking loss [g kg−1 ] Water diffusivity [m2 s−1 ] Internal diameter of each pasta piece [mm] Tube diameter at the base of each rib [mm] External diameter of each pasta piece [mm] Effective energy consumed to cook pasta, as defined by Eq. (6) [kWh] Energy supplied by the induction hob to cook pasta [kJ] Energy dissipated by radiation, convection and conduction from the pan and heating source [kJ] Effective power supplied per unit mass of the water-pasta mixture undergoing cooking [W kg−1 ] Specific energy consumed per unit mass of dry pasta [Wh g−1 ] Cooked pasta hardness at 40% compression (first bite) [N] Cooked pasta hardness at 98% compression (second bite) [N] Heat Fourier number of cooked pasta (=␣t/sCP 2 ) [dimensionless] Mass Fourier number of cooked pasta (=DW t/sCP 2 ) [dimensionless] Greenhouse gas Emission factor referred to the average Italian thermo-electric production from nonrenewable and renewable sources [kg CO2e k Wh−1 ] Enthalpy of cooking water at T0 [kJ] Enthalpy of raw pasta at T0 [kJ] Enthalpy of the pan, lid and stirrer at T0 [kJ] Enthalpy of water evaporated at Tb [kJ] Enthalpy of cooked pasta at Tb [kJ] Enthalpy of the pan, lid and stirrer at Tb [kJ] Enthalpy of pasta water at Tb [kJ] Rib height [mm] Rib hypotenuse [mm] Length of each pasta piece [mm] Mass of cooked pasta [kg] Mass of dried pasta [kg] Mass of the pan, lid and stirrer [kg] Mass of each pasta piece [kg] Mass of water evaporated, as defined by Eq. (3) [kg] Mass of pasta water [kg] Mass of water adsorbed by dry pasta, as defined by Eq. (2) [kg] Initial mass of cooking water [kg] Overall number of pasta pieces in a given amount of raw pasta [dimensionless]

nR PC PH p RCP r2 SGD Sp sCP sF Tb T0 TA TOM t tRu tRb Vc VW WPR WPRmin WPR* WU xPR xS

151

Number of ribs per each pasta piece [dimensionless] Effective power supplied during pasta cooking [kW] Power supplied during water heating [kW] Probability level Cooked pasta resilience (=AD /AC ) [dimensionless] coefficient of determination Degree of starch gelatinisation [%] External surface of each pasta piece [dm2 ] Cooked pasta thickness [mm] Water layer thickness [dm] Boiling point of water [◦ C] Initial temperature [◦ C] Texture analysis Total organic matter [g kg−1 ] Cooking time [s] Above rib thickness [mm] Below rib thickness [mm] Overall volume of all the internal cavities of each pasta piece [L] Minimum volume of water needed to cook homogeneously cooked pasta [L] Water-to-dried pasta ratio [L kg−1 ] Minimum cooking water-to-pasta ratio, as defined by Eq. (13) [L kg−1 ] Effective minimum cooking water-to-pasta ratio, as defined by Eq. (15) [L kg−1 ] Relative increase in cooked pasta mass [ g−1 ] Raw protein content in raw pasta [g kg−1 ] Starch content in raw pasta [g g−1 ]

Greek Symbols Thermal diffusivity of cooked pasta [m2 s−1 ] ␣ Hgel Gelatinisation enthalpy of wheat starch [kJ kg−1 ] Energy efficiency of the cooking system [dimen␩C sionless] ␩IG Electric energy loss of the Italian grid [dimensionless] ␩WE Fraction of water evaporated (=mWE /mW0 ) [dimensionless] ␩WPA Fraction of water adsorbed by cooked pasta (=mWPA /mW0 ) [dimensionless] Fraction of pasta water (=mWF /mW0 ) [dimen␩WF sionless] ␳W Water density [kg L−1 ]

tion hob resulted to be the most efficient cooking system and thus is regarded as an option for cleaner and safer cooking way in developing countries (Banerjee et al., 2016). Moreover, its usage, as coupled to an environmentally sustainable pasta cooking practice previously set up, allowed useless water evaporation to be limited and consequently pasta cooking carbon footprint to be reduced to 0.67 ± 0.04 kg CO2e per kg of dry pasta (Cimini and Moresi, 2017). Greater energy saving was achieved by reducing WPR from the typical values of 10–12 L kg−1 to as low as 2 L kg−1 , the chemico-physical quality of cooked spaghetti being not practically affected (Cimini et al., 2019b). Moreover, no difference in the sensory attributes of firmness, stickiness, bulkiness, and overall cooking quality of a few commercial cooked spaghetti was also detected at WPR equal to 10 or 3 L kg−1 (Cimini et al., 2019a).

152

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

In principle, the minimum WPR value should vary with the pasta shape. In particular, the shapes (i.e., spaghetti with an average diameter of 2 mm) exhibiting a greater external surface-to-volume ratio than others (i.e., rigatoni or helicoidal having an outer diameter of 15–19 mm) are generally more liable to come across and adhere to each other. The importance of such a problem is low at the high cooking water-to-dry pasta ratios generally recommended by pasta makers (i.e., 10–12 L kg−1 ), but it enhances as WPR is reduced. The primary aim of this work was to assess the effect of WPR in the range of 3–10 L kg−1 on the chemico-physical cooking quality of three commercial durum wheat semolina dried pastas of the Penne rigate type (i.e., a short pasta type shaped as medium-length grooved tubes with ridges, cut diagonally at both ends), all being extruded through the same bronze die and dried under almost the same thermo-hygrometric conditions. The secondary aim was to establish an empirical relationship to evaluate the minimum WPR ratio sufficient to avoid short pasta strands sticking to each other. Also the data collected in previous studies carried out on long pasta were used to check for the validity of such a relationship. Finally, the GHG emissions that might be avoided by adopting a more proper cooking procedure were estimated.

2.

Materials and Methods

2.1.

Raw materials

Three commercial brands of durum wheat semolina dried short pasta of the Penne rigate type were kindly provided by De Matteis Agroalimentare Spa (Flumeri, AV, Italy). Their main characteristics are summarised in Table 1. These products were extruded using the same bronze die (external diame® ter 600 mm; thickness 140 mm; Teflon insert no. 330; hole diameter 10.1 mm) under the operating conditions listed in Table 1. Analytical grade chemicals were provided by Carlo Erba (Milan, Italy) or Sigma-Aldrich Srl (Milan, Italy).

2.2.

Equipment and experimental procedure

The pasta cooking system used essentially consisted of a 3-L pan, an induction hob and a stirrer equipped with an S-shaped rod impeller, as previously described (Cimini and Moresi, 2017; Cimini et al., 2019b). All cooking tests were performed using an amount (mW0 ) of bottled still mineral water with a fixed residue at 180 ◦ C of about 300 mg L−1 , as suggested by ISO (2016). After having filled the pan with the aforementioned water at about 20 ◦ C and covered the pan with a lid, the induction stove was regulated at its maximum power rating (2.0 kW) to make water boil rapidly (Cimini and Moresi, 2017; Cimini et al., 2019a, 2019b). The amount of dried pasta (mPA ) added to the boiling water was calculated as reported before (Cimini et al., 2019b) to supply the same effective cooking power per unit mass of water-pasta mixture (eCE ). As water had newly reached the boiling point, the stove control knob was set at 0.4 kW to maintain the cooking water temperature around 98 ◦ C. Such a pasta-cooking phase was performed with the lid closed for as long as the cooking time recommended by the pasta maker (Table 1). The electric energy supplied by the induction hob (ES ) was measured using a digital power meter type RCE MP600 (RCE Srl, Salerno, Italy). At the highest WPR used here (i.e., 10 L kg−1 ), the cooking tests were carried out in the absence of stirring. To limit pasta adhesion during cooking, after several trials the aforementioned stirrer was kept rotating at its minimum level (i.e., 50 rev min−1 ) for 30 s, but resting for the subsequent 90, 60 or 30 s at WPR equal to 6, 4, or 3 L kg−1 , respectively.

2.3.

Raw and cooked pasta analyses

The moisture, total protein (N × 6.25), and ash contents of raw pasta samples were determined via ICC Standard methods No. 110/1, 105/2, and 104/1, respectively (ICC, 1995). The fat, total starch and total dietary fibre contents were assayed using the AACC method 30–20 (AACC International, 2000), and K-TSTA and K-TDFR assay kits (Megazyme Bray, Co. Wicklow, Ireland), respectively. To assess the soluble sugar content of raw pasta, a 5-g sample was charged into a 100-mL conical flask and 70 mL of distilled water were added. After the mixture had been stirred for 10 min, Carrez Reagents I (5 mL) and II (5 mL) were gradually added and the final volume was adjusted to 100 mL with distilled water. The resulting solution was stirred for 2–3 min, and filtered through a filter paper. The filtrate was centrifuged at 10,000 × g for 2 min, and the supernatant was analysed through an HPAEC-PAD system (Dionex Corporation, Sunnyvale, CA, USA) composed of a gradient pump (mod GP50) with an on-line degasser and an electrochemical detector (model ED40). The chromatographic separation was carried out in accordance with Messia et al. (2016). A few short pasta strands were collected at different cooking times up to and beyond the set time suggested by the pasta maker, and immediately cut at right angles with a cutter to detect the presence of the central white annular portion. The time required for such a continuous white line to disappear represented the so-called optimum cooking time (ISO, 2016). Actually, when a broken white line was visible, the cooking process was regarded as completed, the resulting pasta being described as cooked “al dente”. All three commercial brands examined were cooked al dente after a cooking time of 11 min. Cooked pasta pieces were recovered from the pasta water by means of a colander, and cooled by running tap water for 60 s. Excess water was then drained by shaking the colander for 10 s. The water uptake by cooked pasta (WU) was expressed as the difference between the mass of cooked pasta (mCP ) and that of raw pasta (mPA ) divided by mPA . The starch gelatinisation degree (SGD) was colorimetrically measured as described by Cocci et al. (2008). After having brought the mass of the pasta water recov˜ g) were ered to its initial value (mW0 ), some aliquots (10 collected under mixing, and dried to constant weight at 105 ◦ C overnight. The ratio between the masses of residue and raw dried pasta used (mPA ) represented the cooking loss (CL), that is the amount of solid dispersed in the cooking water (D’Egidio et al., 1990). The ICC Standard method n. 153 (ICC, 1995) was used to measure the amount of total organic matter (TOM) found in washing water after pasta cooking with a nominal water-todried pasta ratio of 10 L kg−1 . The textural properties of cooked pasta were determined using a Universal Testing Machine UTM mod. 3342 (Instron Int. Ltd., High Wycombe, UK), equipped with a 1000-N load cell. Three pieces of cooked pasta were inserted into three stainless steel bars (see Fig. S1a in the electronic supplement), and tested using the cutting probe, shown in Fig. S1bc. Each Texture Analysis (TA) test was performed as described by Ballestrieri et al. (2015). The difference between the total displacement of the probe at the contact points with the three aligned bars and pasta samples, both revealed by a trigger force of 0.05 N, allowed the average thickness (sCP ) of cooked Penne rigate to be estimated. After setting the probe speed at

153

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Table 1 – Main production conditions, characteristics and chemical composition of the three commercial penne rigate brands used in this work. Pasta Brand Pasta production conditions Production Lot Kneading water fraction [%] Extrusion flow rate [Mg h−1 ] Temperature at the pasta shaker [◦ C] Pre-drying temperature [◦ C] Pre-drying relative humidity variation [%] Drying temperature [◦ C] Drying relative humidity variation [%] Main Characteristics Penne length, Lp [mm] Penne internal diameter, di [mm] Penne external diameter, dRu [mm] Above rib thickness, tRu [mm] Below rib thickness, tRb [mm] 100-piece weight [g] Cooking time suggested by pasta maker [min] White core disappearance time [min] Total organic matter, TOM [g kg−1 ] Chemical Composition [g kg−1 ] Moisture Raw protein (N × 6.25) Starch Soluble sugars Total fibres Fat Ash

A

B

C

L7265H 32 5.56 100 87 16 86 8.5

L7287H 28 6.05 92 87 17.5 89 8.3

L7282L 28 6.05 92 87 17.5 89 8.3

36 ± 2 5.08 ± 0.07 8.88 ± 0.04 1.90 ± 0.04 1.50 ± 0.14 150 ± 8 10-12 11.00 6.9 ± 0.8

36 ± 2 5.07 ± 0.08 8.94 ± 0.05 1.94 ± 0.05 1.42 ± 0.18 158 ± 13 10-12 11.50 6.5 ± 0.5

34 ± 2 5.02 ± 0.06 8.93 ± 0.06 1.96 ± 0.04 1.43 ± 0.20 149 ± 9 10-12 14.50 6.2 ± 0. 5

124 ± 0.5 108 ± 2 665 ± 3 37.0 ± 1.5 26 ± 3 21 ± 4 5.2 ± 0.3

114 ± 0.4 119.0 ± 0.2 682 ± 7 32.0 ± 0.5 25.0 ± 0.6 13.0 ± 0.3 5.1 ± 0.5

116 ± 2 131 ± 1 657 ± 2 31.0 ± 0.4 27.0 ± 1.5 17.0 ± 0. 9 5.3 ± 0.3

1 mm s−1 , a first compression cycle was carried out to submit the pasta pieces to a 40% compression with respect to sCP (first bite). The probe was then lifted up to its initial position. After a pause of 5 s, it was moved downward to submit the pasta pieces to a second 98% compression (second bite). Finally, the cutting probe was raised to reach again its initial position. The force peak on the 1st or 2nd compression cycle represented the pasta hardness at 40% (F40 ) or 98% (F98 ) deformation. The cooked pasta resilience (RCP ) was estimated as the ratio between the force-vs-time areas during the withdrawal (AD ) and compression (AC ) of the probe in the first bite. Each TA test was repeated five times. No statistically significant difference among the above TA parameters (p = 0.05) was noticed if the time delay between cooked pasta draining and testing was no longer than 10 min.

Fig. 1 – Block diagram of the pasta cooking system used to establish the mass and energy balances. All symbols were listed in the Nomenclature section. The water adsorbed by cooked pasta (mWPA ) and that evaporated (mWE ), as well as their relative fractions (␩Wi ) with respect to mW0 , were estimated as:

2.4. Water and energy balances during the pasta cooking process

mWPA = mCP -mPA Fig. 1 shows the block diagram used to establish the mass and energy balances. Differently from previous work (Cimini et al., 2019a, 2019b), the cooking water mass was only measured at the beginning (mW0 ) and end of the cooking process, once cooked pasta (mCP ) had been strained from pasta water (mWF ). In particular, the mass balance was written as

(2)

and mWE = mW0- mWF- mWPA

(3)

The energy balance was expressed as follows: HW0 +HPA +HPLS +ES = HWEb +HCPb +HPLSb +HWFb

mW0 + mPA + mPLS = mWE + mCP + mPLS + mWF

(1)

where mPA is the mass of raw pasta used in each trial, mPLS the mass of the pan, lid and stirrer, and mWE the mass of water evaporated throughout the cooking process.

+mPA xS Hgel +ERC

(4)

where HW0 , HPA , and HPLS are the enthalpies of cooking water, raw pasta, and pan, lid and stirrer, all at the initial temperature T0 ; ES is the energy supplied by the induction hob; HWEb , HCPb , HPLSb, and HWFb are the enthalpies

154

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

of water evaporated, cooked pasta, pan, lid and stirrer, and pasta water, all at the boiling point Tb ; ERC is the energy dissipated by radiation, convection and conduction from the pan and heating source. Since pasta cooking involves starch gelatinisation, the heat of starch gelatinisation was estimated as the mass fraction of starch (xS ) in raw pasta times its mass (mPA ) and enthalpy of wheat starch gelatinisation (Hgel = 11.9 J g−1 ), the latter being extracted from Ratnayake et al. (2009). The energy efficiency (␩C ) of the pasta cooking system was calculated as the enthalpy change for cooking water, raw pasta, as well as pan, lid and stirrer, from T0 to Tb , plus the heat of starch gelatinisation divided by ES .

2.5.

Carbon footprint of pasta cooking

According to the Product Environmental Footprint category rules for dry pasta (UNAFPA, 2018), the carbon footprint of pasta cooking (CFPC ) is mainly dependent on the electric energy requirements to cook pasta. Thus, it was estimated by multiplying the overall electric energy consumed (EPC ) by its 100-year time horizon Global Warming Potential factor (GWPEE ): CFPC = EPC GWPEE

(5)

with EPC = ePA /(1-␩IG )

(6)

where ePA is the specific energy consumed to cook 1 kg of raw pasta, while GWPEE (=0.3213 kg CO2e k Wh−1 ) and ␩IG (≈5.8%) were referred to the average thermo-electric production from non-renewable and renewable sources and electric energy loss of the Italian grid in 2016, as extracted from ISPRA (2018). In compliance with the PAS 2050 standard method (BSI, 2008), the production of capital goods (machinery, equipment and energy wares) was excluded from the system boundary.

2.6.

Statistical analysis of data

Each cooking test was repeated three times. Each parameter was shown as average ± standard deviation and was analyzed by Tukey test at a probability level (p) of 0.05. One-way analysis of variance was carried out using SYSTAT version 8.0 (SPSS Inc., 1998).

3.

Results and discussion

3.1.

Main short pasta characteristics

Table 1 lists the main physico-chemical characteristics and production conditions for the three commercial brands under study. All pasta samples were dried at temperatures falling within the typical range (85–96 ◦ C) used in the very-high-temperature pasta drying processes (Milatovich and Mondelli, 1990; Zweifel et al., 2003). They were extruded through the same bronze die, equipped with Teflon® inserts, and thus exhibited almost the same internal and external diameters, and length, these being approximately equal to 5.0, 8.9, and 36 mm, respectively. Their 100-piece weight was about 150 g for samples 1 and 3, and 158 g for sample 2 (Table 1).

The raw protein content was expressed as total nitrogen × 6.25, as reported in pasta labels (EU Regulation No. 1169/2011), and was found to vary from 108 to 131 g kg−1 The average moisture, raw protein, starch, fat and ash contents did not differ from those of typical durum wheat semolina dry ´ pasta (Biernacka et al., 2018; Pasqualone et al., 2014; Piwinska et al., 2016). The amount of total organic matter (TOM) leached after thorough rinsing of drained, cooked pasta was determined as an indirect measure of the sensory attributes of stickiness and overall cooking quality (Cubadda et al., 2007, 2009). Since all TOM values shown in Table 1 were by far smaller than 14 g kg−1 (ICC Standard no. 153, 1995), all samples appeared to be of good quality. As the raw protein content increased from 108 to 131 g kg−1 , the time at which the central white core of a short strand of cooked pasta fully disappeared increased from 11.0 to 14.5 min (Table 1). Moreover, the short strands of pasta type 3 exhibited almost the same broken central white line after about 11 min whatever the cooking water-to-dried pasta ratio used (i.e., 10, 6 or 3 L kg−1 ), as shown in Fig. S2 in the electronic supplement. The same optimal cooking time was assessed even for the short strands of the other pasta types examined (data not shown for the sake of simplicity). Thus, all pasta cooking tests lasted 11 min whatever the short pasta type and WPR used. As reported in the literature (Cunningham et al., 2007; Fasano et al., 2011), pasta cooking is firstly controlled by water penetration and then by starch gelatinisation. A few seconds after pasta immersion in boiling water, the water penetrates the pasta surface, giving rise to a water penetration front and a relatively stiff unpenetrated core. Consequently, the moisture content (xW ) within the pasta strand is higher at the pasta surface and smaller at its centre, as also confirmed by magnetic resonance imaging (Maeda et al., 2009). Starch gelatinisation was found to start at xW > 0.23 g g−1 , reach a maximum value at xW = 0.6 g g−1 , and remain constant for xW > 0.6 g g−1 (Lund and Lorenz, 1984). During cooking, the short pasta cross section exhibited three different annular regions separated by visible interfaces. A whitish central annulus, not yet soaked, was surrounded by an annulus of neutral colour, and then by an external annulus, in which gelatinisation was taking place (Fasano et al., 2011; Sicignano et al., 2015). Such a structure was still visible at the end of the cooking process, thus the so called pasta “al dente” was still far from being fully gelatinised (Fasano et al., 2011). In boiling water, the change in temperature and moisture distribution in the pasta strand is due to heat conduction and water diffusion, respectively. The dimensionless temperature (or moisture) ratio within the pasta strand exponentially decays as the heat, Foh , (or mass, Fom ) Fourier number increases (Carslaw and Jaeger, 1959; Crank, 1975). After a cooking time of 11 min, cooked pasta exhibited an average moisture content of about 0.65 g g−1 , as also assessed by Sobota and Zarzycki (2013). Thus, the cooked pasta thermal (␣) or water (DW ) diffusivity was of the order of 1.31 × 10−7 m2 s−1 (Shafiur Rahman, 2009), and (4–7) × 10−10 m2 s−1 (Horigane et al., 2006; Maeda et al., 2009), respectively. Since the ratio between the heat and mass Fourier numbers coincides with the ␣/DW ratio, this varying from 187 to 327, the cooking process was regarded as essentially isothermal and controlled by water migration.

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Fig. 2 – Cooking tests using the induction hob set at different power levels during the cooking water heating (PH : red-filled symbols) and pasta cooking (PC : yellow-filled symbols) phases with three short pasta brands labelled A (open or closed squares), B (open or closed triangles), and C (open or closed circles): Specific energy consumed to cook dry pasta (ePA : open symbols) and cooking energy efficiency (␩C : closed symbols) versus the cooking water-to-dried pasta ratio (WPR). The inclined broken line was plotted using Eq. (7), while the horizontal broken ones represent the average values of PC and PH , respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

3.2.

Electric energy requirements

All the energy-related parameters collected during the cooking tests are listed in the electronic supplement (Table S1). The electric power supplied by the induction hob (ES ) was found to be a linear function of the cooking time (t), such a relationship being characterized by a coefficient of determination (r2 ) greater than 0.99. The proportionality coefficient coincided with the power supplied during the water heating phase (PH ) or pasta cooking one (PC ). As shown in Fig. 2, PH and PC were approximately constant and equal to 1.85 ± 0.02 or 0.24 ± 0.01 kW, respectively. Moreover, the effective power supplied per unit mass of the water-pasta suspension undergoing cooking (eCE ) was nearly constant (149 ± 3 W kg−1 ) independently of WPR (Table S1). On the contrary, the specific energy consumed per unit mass of raw pasta (ePA ) linearly increased from about 0.52 to 1.58 Wh g-1 as WPR was increased from 3 to 10 L kg−1 . The broken line plotted in Fig. 2 represented the least squares regression: ePA = (0.153 ± 0.004)WPR(r2 = 0.992)

(7)

To prevent short pasta strands from sticking one another, the stirrer was kept rotating at 50 rev min−1 for 30 s and resting for the aforementioned times at WPR < 10 L kg−1 . In all cases, the electric power supplied was of the order of 6.1 ± 0.5 W, as measured via the digital power meter. Thus, the overall mixing energy consumed throughout a cooking time of 11 min was estimated as equal to 0.28, 0.37 or 0.56 Wh for WPR = 6, 4, or 3 L kg−1 , respectively. The specific stirring energy consumed to cook the different amounts of raw pasta increased from 1.2 to 1.4 Wh kg−1 . Thus, the energy needed to stir pasta during its

155

Fig. 3 – Cooking tests carried out with three short pasta brands labelled A (open or closed squares), B (open or closed triangles), and C (open or closed circles): Percentages of water evaporated (␩WE : red-filled symbols), adsorbed by cooked pasta (␩WPA : open symbols), and recovered as pasta water (␩WF : closed symbols) vs. WPR. The dashed lines represent the least squares regression lines for the three parameters examined. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

cooking was insignificant with respect to that (ePA ) needed to cook it (Table S1). Finally, the overall cooking energy efficiency (␩C ) was about constant (67 ± 2 %) whatever the WPR used and agreed with that previously observed when cooking another type of short pasta (i.e., helicoidal) with the same eco-sustainable procedure at WPR = 12 L kg−1 (Cimini and Moresi, 2017).

3.3.

Cooking water use

As WPR was reduced from 10 to 3 L kg−1 , the percentage of water evaporated during the cooking process (␩WE ) increased from 4.4 ± 0.1 to 6.5 ± 0.7% of that initially added into the pot (mW0 ), while that absorbed by cooked pasta (␩WPA ) increased from 10.2 ± 0.1 to 33 ± 2% (Fig. 3). Conversely, the fraction of pasta water (␩WF ) reduced from 85.4 ± 0.1 to 60.2 ± 0.9% of mW0 . Thus, even at WPR = 3 L kg−1 , there was no shortage of water at the end of pasta cooking. By referring to previous work on commercial spaghetti cooking (Cimini et al., 2019a), at WPR = 3 L kg−1 the fraction of water absorbed by cooked spaghetti amounted to 41 ± 4% of mW0 , probably because spaghetti exhibited a higher water uptake (i.e., 1.27–2.96 g g−1 ) than the short pastas used here (see below).

3.4.

Cooked pasta quality

As concerning cooked pasta quality, Table S1 shows the mean values and standard deviations of all parameters determined. As shown in Fig. 4, the relative water uptake (WU), degree of starch gelatinisation (SGD), and cooking loss (CL) as referred to cooked pasta appeared to be independent of WPR and pasta type, being equal to 1.03 ± 0.01 g g−1 , 8.2 ± 0.7%, and 3.5 ± 0.4 g kg−1 , respectively. The observed WU and CL values

156

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Fig. 4 – Cooking tests carried out with three short pasta brands labelled A (open or closed squares), B (open or closed triangles), and C (open or closed circles): Relative water uptake (WU: red-filled symbols), cooking loss (CL: open symbols), and degree of starch gelatinisation (SGD: closed symbols) vs. WPR. The continuous lines refer to the mean values of WU, CL, and SGD, while the broken ones to the ±5 or 10% deviation bands. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

Fig. 5 – Texture Analysis tests carried out with three cooked short pasta brands labelled A (open or closed squares), B (open or closed triangles), and C (open or closed circles): Cooked pasta hardness at 40% (F40 : closed symbols) and 98% (F98 : open symbols) compression, resilience (RCP x10: yellow-filled symbols), and thickness (sCP : red-filled symbols) vs. protein content of raw pasta (xPR ). The broken lines were plotted using Eqs. (8) and (9), while the continuous ones show the mean values of RCP , and sCP . (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article).

3.6. agreed with those ascertained in good quality pasta by other authors (Pasqualone et al., 2016).

3.5.

Textural properties of cooked pasta

Table S2 shows the main results of the TA tests. Within the range of WPR values examined, all TA parameters, as well as the thickness (sCP ) of cooked pasta, exhibited a statistically insignificant variation at the probability level of 0.05 (Table S2). However, the cooked pasta hardness at 40% (F40 ) or 98% (F98 ) compression appeared to be a function of raw pasta formulation. In particular, F40 and F98 were correlated to the protein content of raw pasta (xPR ) by using the least squares method. Both parameters resulted to increase almost linearly with xPR , as shown by the broken lines plotted in Fig. 5: F40 = (−1.5 ± 0.9) + (0.040 ± 0.075)xPR (r2 = 0.76)

(8)

F98 = (−8.1 ± 0.9) + (0.151 ± 0.019)xPR (r2 = 0.86)

(9)

This paralleled the relationship between the protein content of raw pasta and cooked pasta firmness, previously established by Cubadda et al. (2007), as well as that relating the sensory attributes of Firmness, Stickiness, Bulkiness, and Overall Cooking Quality of cooked pasta to xPR (Cimini et al., 2019a). On the contrary, as shown by the continuous lines plotted in Fig. 5, the cooked pasta resilience (RCP ) and thickness (sCP ) appeared to be independent of xPR , and equal to 0.55 ± 0.02 and 2.14 ± 0.09 mm, respectively.

Carbon footprint of pasta cooking

Since the carbon footprint of pasta cooking (CFPC ) is a linear function of ePA , as shown by Eqs. (5) and (6), and, in turn, ePA relies on WPR only, as shown by Eq. (7), CFPC was predicted as follows: CFPC = (0.052 ± 0.001)WPR

(10)

By resorting to the eco-sustainable cooking procedure used here, it was possible to reduce CFPC from 0.49 ± 0.04 to 0.18 ± 0.01 kg CO2e kg−1 of raw pasta as WPR was lowered from 10 to 3 L kg−1 , respectively. The estimated CFPC scores were definitively smaller than those associated to home pasta cooking using a conventional gas- or electric-fired stove (UNAFPA, 2018). More specifically, for WPR = 3 L kg−1 the cradle-to grave carbon footprint of raw pasta might be practically halved from 3.03 (Barilla, 2017) to 1.51 kg CO2e kg−1 and the overall GHG emissions associated with the current Italian consumption of ˜ × 106 Mg/year) might be cut by as much as 2.1 Tg dry pasta (1.4 CO2e yr−1 , thus approximately halving the climate change potential of dried pasta.

3.7.

Empirical prediction of the minimum WPR

Theoretically, the minimum volume of water (VW ) to pour into a pan to cook as homogeneously as possible a given type of long or short pasta is related to the packing arrangement of the pasta items and water uptake (WU) by cooked pasta. Moreover, VW should also fill all the internal cavities (if present) of each pasta strand. Thus, VW can be expressed as VW = np Vc + mPA WU/␳W

(11)

157

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

with mPA = np mp

(12)

Table 2 – Schematic scheme of the cross section of a pasta piece of the penne rigate type and its main geometric characteristics to be directly measured or calculated.

where, np is the overall number of pasta pieces present in a given amount of raw pasta (mPA ), mp the mass of a single pasta piece, Vc the overall volume of all internal cavities, and ␳W the density of water. Thus, the minimum cooking water-to-dry pasta ratio (WPRmin ) can be estimated as:

WPRmin = VW /mPA =

Vc WU + mp W

(13)

By referring to the internal diameter (di ) and length (Lp ) of each pasta piece of the three brands used here (Table 1), as well as the average specific water uptake (WU) (see Table S1), the estimated WPRmin ranged from 1.43 to 1.52 L kg−1 . By applying Eq. (13) to the commercial spaghetti used in previous researches, where spaghetti diameter and WU varied in the ranges of 1.9–2.0 mm, and 1.25–2.96 g g−1 , respectively (Cimini et al., 2019a, 2019b), WPRmin fluctuated from about 1.25 to 3.0 L kg−1 . In the circumstances, such a theoretical minimum amount of cooking water does not guarantee a uniform wetting of the external surface of all pasta pieces and, thus, does not avoid pasta sticking during cooking. To assure a good distribution of heat and water over the pasta surface, the external surface (Sp ) of each pasta piece was assumed to be covered with a suitable layer (sF ) of water. Consequently, the minimum water volume increases to: VW ∗ = np Sp sF + np Vc + mPA WU/␳W

Cross section scheme hR = tRu - tRb = dRu - dRb

Number of ribs per piece

nR =

Rib base

bR =

Rib hypotenuse

iR =

External surface of each piece Volume of cylinder cavity

Sp = 2 nR iR Lp + 2 [ 4

4.

Sp sF Vc WU + + mp mp W

R

 dRb nR

  2 b

Vc =

R 2

2

+ hR



2

2

dRb − di



+ nR

bR hR 2

]

 2 4 di Lp

(14)

Thus, the effective minimum cooking water-to-dry pasta ratio (WPR* ) becomes

WPR∗ =

d   h Rb 

Rib height

(15)

Table 2 shows a schematic diagram of the cross section of the pasta type examined in this work, as well as the relationships needed to estimate all the above geometric parameters. By accounting for the geometric characteristics of the three commercial short pastas used (Table 1), each pasta piece would have been uniformly covered with 1.51- or 1.58-mm thick layer of water if one kg of raw pasta was cooked with just 3 L of cooking water. In the case of the raw spaghetti 1.94 mm in diameter with WU = 1.3 ± 0.1 g g−1 , as those previously used (Cimini et al., 2019b), sF would reduce to 1.15 or 0.47 mm if WPR was equal to 3 or 2 L kg−1 , respectively. Whatever the raw short or long pasta type chosen of known geometric characteristics, Eq. (15) might, as a rule of thumb, be used to predict the minimum WPR* value by assuming that a layer of water 1.5 mm in thickness in order to avoid pasta adhesion during cooking in the presence of a proper agitation degree. It is worth noting that the pasta cooking system used here incorporated a stirrer and allowed to cook pasta in a pan covered by a lid in order to save water and energy, whereas pasta is usually cooked in an open pan (without lid) for allowing frequent manual stirring.

Conclusions

Pasta cooking is an energy-intensive process. Its energy requirements might be significantly reduced by lowering concurrently the cooking water-to-dry pasta ratio (WPR) and effective power supplied during the so-called pasta cooking phase. In the case of shot-cut extruded pasta of the Penne rigate type, the instrumental and chemical quality parameters of cooked pasta were almost constant when WPR was varied from 3 to 10 L kg−1 . Obviously, the higher the protein content of raw pasta the higher the hardness of cooked pasta at the first (F40 ) and second (F98 ) bites became. Owing to the great number of commercial pasta types available, it was established an empirical equation to predict which WPR value is needed to avoid pasta adhesion during cooking by resorting to the external surface and empty volume of each pasta piece, and most probable water uptake by cooked pasta. In the circumstances, it was possible to cook one kg of short pasta with just 3 L of water under mild mixing with a minimum energy need of 0.54 ± 0.02 Wh g−1 , this cutting the GHGs emitted to sustain the current consumption of dry pasta by about 50%. Further work is thus required to develop a specialized appliance to cook pasta.

Declarations of interest None.

158

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

Acknowledgements This research was supported by the Italian Ministry of Instruction, University and Research, special grant PRIN 2015 - prot. 2015MFP4RC 002. The authors would like to thank Mr. Gianluca Ianari for his skillful assistance during the experimental work, and Ing. Pietro D’Orsi (De Matteis, Agroalimentare Spa, Flumeri, AV, Italy) for providing the short pasta types examined in this work.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j. fbp.2019.05.004.

References AACC International, 2000. Approved Methods of Analysis. Methods no. 30.20, 66-50.01. American Association of Cereal Chemists Inc., St. Paul. Ballestrieri, D., D’Alessandro, A., Carnevali, P., 2015. Texture Profile Analysis of Cooked Short Pasta. MP-000030 Barilla Tests. Banerjee, M., Prasad, R., Rehman, I.H., Gill, B., 2016. Induction stoves as an option for clean cooking in rural India. Energy Policy 88, 159–167. Barilla, 2017. EPD217 Barilla Durum Wheat Semolina Pasta in Paperboard Box (Accessed 9 January 2019) https://edgeenvironment.com/wp-content/uploads/2016/11/ epd217-Durum-wheat-semoline-dried-pasta.pdf. ˙ Biernacka, B., Dziki, D., Rózyło, R., Wójcik, M., Mi´s, A., Romankiewicz, D., Krzysiak, Z., 2018. Relationship between the properties of raw and cooked spaghetti – new indices for pasta quality evaluation. Int. Agrophys. 32, 217–223, http://dx.doi.org/10.1515/intag-2017-0012. BSI, 2008. Publicly Available Specification (PAS 2050) for the Assessment of the Life Cycle Greenhouse Gas Emission of Goods and Services. British Standards Institution, London, UK. Carslaw, H.S., Jaeger, J.C., 1959. Conduction of Heat in Solids. Oxford Univ. Press, London. Cimini, A., Moresi, M., 2017. Energy efficiency and carbon footprint of home pasta cooking appliances. J. Food Eng. 204, 8–17. Cimini, A., Cibelli, M., Messia, M.C., Marconi, E., Moresi, M., 2019a. Cooking quality of commercial spaghetti: effect of the water-to-dried pasta ratio. Eur. Food Res. Technol. 245, 1037–1045, http://dx.doi.org/10.1007/s00217-018-3205-2. Cimini, A., Cibelli, M., Moresi, M., 2019b. Reducing the cooking water-to-dried pasta ratio and environmental impact of pasta cooking. J. Sci. Food Agriculture 99, 1258–1266, http://dx.doi.org/10.1002/jsfa.9299. Cocci, E., Sacchetti, G., Vallicelli, M., Angioloni, A., Dalla Rosa, M., 2008. Spaghetti cooking by microwave oven: cooking kinetics and product quality. J. Food Eng. 85, 537–546. Crank, J., 1975. The Mathematics of Diffusion. Oxford Univ. Press, London. Cubadda, R., Carcea, M., Marconi, E., Trivisonno, M.C., 2007. Influence of gluten proteins and drying temperature on the cooking quality of durum wheat pasta. Cereal Chem. 84, 48–55. Cubadda, R.G., Iafelice, G., Marconi, E., 2009. Pasta cooking quality and its evaluation. Tecnica Molitoria Int. 60 (10/A), 174–180. Cunningham, S.E., McMinn, W.A.M., Magee, T.R.A., Richardson, P.S., 2007. Modelling water absorption of pasta during soaking. J. Food Eng. 82, 600–607. D’Egidio, M.G., Mariani, B.M., Nardi, S., Novaro, P., Cubadda, R., 1990. Chemical and technological variables and their relationships: a predictive equation for pasta cooking quality. Cereal Chem. 67, 275–281.

EU Regulation No. 1169/2011 of the European parliament and of the council of 25 October 2011 on the provision of food information to consumers. L 304/18. Official J. Eur. Union 22.11.2011. Fasano, A., Primicerio, M., Tesi, A., 2011. A mathematical model for spaghetti cooking with free boundaries. Netw. Heterog. Media 6 (1), 37–60. Horigane, A.K., Naito, S., Kurimoto, M., Irie, K., Yamada, M., Motoi, H., Yoshida, M., 2006. Moisture distribution and diffusion in cooked spaghetti studied by NMR imaging and diffusion model. Cereal Chem. 83 (3), 235–242. ICC, 1995. Standard Methods of the International Association for Cereal Science and Technology. Methods: 110/1, 105/2, 104/1, 153. Vienna, A: ICC. ISO, 2016. Durum Wheat Semolina and Alimentary Pasta Estimation of Cooking Quality of Alimentary Pasta by Sensory Analysis - Part 1: Reference Method. ISO n. 7304-1. CH: International Organization for Standardization, Vernier, Geneva. ISPRA, 2018. Italian Greenhouse Gas Inventory 1990-2016. National Inventory Report 2018. ISPRA Rapporti 283, Rome, Italy (Accessed 8 February 2019) http://www.sinanet.isprambiente.it/it/sia-ispra/serie-storicheemissioni/national-inventory-report-2018/view. Lund, D., Lorenz, K.J., 1984. Influence of time, temperature, moisture, ingredients, and processing conditions on starch gelatinization. CRC Crit. Rev. Food Sci. Nutr. 20 (4), 249–273, http://dx.doi.org/10.1080/10408398409527391. Maeda, I., Horigane, A.K., Yoshida, M., Aikawa, Y., 2009. Water diffusion in buckwheat noodles and wheat noodles during boiling and holding as determined from MRI and rectangular cylinder diffusion model. Food Sci. Technol. Res. 15 (2), 107–116. Messia, M.C., Reale, A., Maiuro, L., Candigliota, T., Sorrentino, E., Marconi, E., 2016. Effects of pre-fermented wheat bran on dough and bread characteristics. J. Cereal Sci. 69, 138–144. Milatovich, L., Mondelli, G., 1990. La Tecnologia Della Pasta Alimentare. Chiriotti Editori, Pinerolo, Italy. Pasqualone, A., Paradiso, V.M., Summo, C., Caponio, F., Gomes, T., 2014. Influence of drying conditions on volatile compounds of pasta. Food Bioprocess Technol. 7, 719–731. Pasqualone, A., Gambacorta, G., Summo, C., Caponio, F., Di Miceli, G., Flagella, Z., Marrese, P.P., Piro, G., Perrotta, C., De Bellis, L., Lenucci, M.S., 2016. Functional, textural and sensory properties of dry pasta supplemented with lyophilized tomato matrix or with durum wheat bran extracts produced by supercritical carbon dioxide or ultrasound. Food Chem. 213, 545–553. Piazza, L., Riva, M., Masi, P., 1994. Modelling pasta cooking processes. In: Yano, Y., Matsuno, R., Nakamura, K. (Eds.), Developments in Food Engineering. Blackie Academic and Professional, London, UK, pp. 304–306. ´ Piwinska, M., Wyrwisz, J., Kurek, M.A., Wierzbicka, A., 2016. Effect of drying methods on the physical properties of durum wheat pasta. Cyta - J. Food 14 (4), 523–528, http://dx.doi.org/10.1080/19476337.2016.1149226. Ratnayake, W.S., Otani, C., Jackson, D.S., 2009. DSC enthalpic transitions during starch gelatinization in excess water, dilute sodium chloride, and dilute sucrose solutions. J. Sci. Food Agriculture 89, 2156–2164. Shafiur Rahman, M., 2009. Food Properties Handbook, 2nd edn. CRC Press, Boca Raton, FL. Sicignano, A., Di Monaco, R., Masi, P., Cavella, S., 2015. From raw material to dish: pasta quality step by step. J. Sci. Food Agriculture 95 (13), 2579–2587. Sobota, A., Zarzycki, P., 2013. Effect of pasta cooking time on the content and fractional composition of dietary fiber. J. Food Qual. 36, 127–132. UNAFPA (Unions de Associations de Fabricants de Pâtes Alimentaires), 2015a. Statistics – World Pasta Production (Accessed 8 February 2019) http://www.pasta-unafpa.org/ingstatistics5.htm.

Food and Bioproducts Processing 1 1 6 ( 2 0 1 9 ) 150–159

UNAFPA, 2015b. Statistics – Pasta Consumption (Accessed 8 February 2019) http://www.pasta-unafpa.org/ingstatistics4.htm. UNAFPA, 2018. Product Environmental Footprint Category Rules (PEFCR) for Dry Pasta. Vers. 3 (Accessed 10 February 2019) http://ec.europa.eu/environment/eussd/smgp/pdf/Dry %20pasta%20PEFCR final.pdf.

159

Worldometers, 2019. Italy Population (Live) (Accessed 8 February 2019) http://www.worldometers.info/world-population/italypopulation/. Zweifel, C., Handschin, S., Escher, F., Conde-Petit, B., 2003. Influence of high-temperature drying on structural and textural properties of durum wheat pasta. Cereal Chem. 80 (2), 159–167, http://dx.doi.org/10.1094/CCHEM.2003.80.2.159.