Loading [Contrib]/a11y/accessibility-menu.js
Research article

Effect of weather on monthly electricity consumption in three coastal cities in West Africa

  • In several regions worldwide, demand for electricity can be highly dependent on weather conditions. This study investigates the relationships between weather and electricity consumption in three West African cities. Monthly electricity consumption datasets for the cities of Abidjan (Ivory Coast), Cotonou (Benin) and Lomé (Togo) for the 1990–2015, 2000–2015 and 2008–2014 periods respectively were collected from national electricity companies, and meteorological data of the synoptic stations were used to compute Cooling Degree-Days in the three cities. The Cooling Degree-Days indices were estimated using air temperature and two temperature indices (the Humidex and the Heat Index). For the statistical analysis, classical multiplicative decomposition was applied to consumption data for subperiods for which consumption was considered to show relatively homogeneous evolutionary behavior (Abidjan and Lomé from 2011 to 2014 and Cotonou from 2009 to 2014). Regardless of the temperature indices considered in the three cities, the Cooling Degree-Days indices are well correlated with the seasonal variability of power consumption and particularly, the peak consumption observed in March and the lower consumption in August. Slightly better correlations are obtained for Cotonou and Abidjan when the heat index (combining both temperature and relative humidity) are used to calculate the Cooling Degree-Days.

    Citation: Ghafi Kondi Akara, Benoit Hingray, Adama Diawara, Arona Diedhiou. Effect of weather on monthly electricity consumption in three coastal cities in West Africa[J]. AIMS Energy, 2021, 9(3): 446-464. doi: 10.3934/energy.2021022

    Related Papers:

    [1] Cíntia Sorane Good Kitzberger, David Pot, Pierre Marraccini, Luiz Filipe Protasio Pereira, Maria Brígida dos Santos Scholz . Flavor precursors and sensory attributes of coffee submitted to different post-harvest processing. AIMS Agriculture and Food, 2020, 5(4): 700-714. doi: 10.3934/agrfood.2020.4.700
    [2] Ioannis K. Karabagias, Chara Papastephanou, Vassilios K. Karabagias . Geographical differentiation of Cypriot multifloral honeys through specific volatile compounds and the use of DFA. AIMS Agriculture and Food, 2019, 4(1): 149-162. doi: 10.3934/agrfood.2019.1.149
    [3] Cíntia Sorane Good Kitzberger, Maria Brígida dos Santos Scholz, João Batista Gonçalves Dias da Silva, Marta de Toledo Benassi, Luiz Filipe Protasio Pereira . Free choice profiling sensory analysis to discriminate coffees. AIMS Agriculture and Food, 2016, 1(4): 455-469. doi: 10.3934/agrfood.2016.4.455
    [4] Sapta Raharja, Yogi Purna Rahardjo, Samsudin, Khaswar Syamsu . Aroma precursor enhancing in dried cocoa beans fermentation using enzyme and heat addition. AIMS Agriculture and Food, 2023, 8(2): 674-686. doi: 10.3934/agrfood.2023037
    [5] Dody Dwi Handoko, Anisa Maharani Kaseh, Laras Cempaka, Wahyudi David, Bram Kusbiantoro, Afifah Zahra Agista, Yusuke Ohsaki, Hitoshi Shirakawa, Ardiansyah . Effects of household-scale cooking on volatile compounds, sensory profile, and hypotensive effect of Kenikir (Cosmos caudatus). AIMS Agriculture and Food, 2023, 8(1): 198-213. doi: 10.3934/agrfood.2023011
    [6] Ardiansyah, Fauziyyah Ariffa, Rizki Maryam Astuti, Wahyudi David, Dody Dwi Handoko, Slamet Budijanto, Hitoshi Shirakawa . Non-volatile compounds and blood pressure-lowering activity of Inpari 30 and Cempo Ireng fermented and non-fermented rice bran. AIMS Agriculture and Food, 2021, 6(1): 337-359. doi: 10.3934/agrfood.2021021
    [7] Siska Septiana, Nancy Dewi Yuliana, Boy Muchlis Bachtiar, Christofora Hanny Wijaya . Aroma-active compounds of Melaleuca cajuputi essential oil, a potent flavor on Cajuputs Candy. AIMS Agriculture and Food, 2020, 5(2): 292-306. doi: 10.3934/agrfood.2020.2.292
    [8] Esteban Largo-Avila, Carlos Hernán Suarez-Rodríguez, Jorge Latorre Montero, Madison Strong, Osorio-Arias Juan . The influence of hot-air mechanical drying on the sensory quality of specialty Colombian coffee. AIMS Agriculture and Food, 2023, 8(3): 789-803. doi: 10.3934/agrfood.2023042
    [9] Eleni C. Pappa, Efthymia Kondyli, Anna-Maria Vlachou, Athanasia Kakouri, Evdokia Malamou, John Samelis . Semi industrial production of Tsalafouti dairy product. AIMS Agriculture and Food, 2022, 7(2): 444-460. doi: 10.3934/agrfood.2022028
    [10] Cíntia Sorane Good Kitzberger, Maria Brígida dos Santos Scholz, Luiz Filipe Protasio Pereira, João Batista Gonçalves Dias da Silva, Marta de Toledo Benassi . Profile of the diterpenes, lipid and protein content of different coffee cultivars of three consecutive harvests. AIMS Agriculture and Food, 2016, 1(3): 254-264. doi: 10.3934/agrfood.2016.3.254
  • In several regions worldwide, demand for electricity can be highly dependent on weather conditions. This study investigates the relationships between weather and electricity consumption in three West African cities. Monthly electricity consumption datasets for the cities of Abidjan (Ivory Coast), Cotonou (Benin) and Lomé (Togo) for the 1990–2015, 2000–2015 and 2008–2014 periods respectively were collected from national electricity companies, and meteorological data of the synoptic stations were used to compute Cooling Degree-Days in the three cities. The Cooling Degree-Days indices were estimated using air temperature and two temperature indices (the Humidex and the Heat Index). For the statistical analysis, classical multiplicative decomposition was applied to consumption data for subperiods for which consumption was considered to show relatively homogeneous evolutionary behavior (Abidjan and Lomé from 2011 to 2014 and Cotonou from 2009 to 2014). Regardless of the temperature indices considered in the three cities, the Cooling Degree-Days indices are well correlated with the seasonal variability of power consumption and particularly, the peak consumption observed in March and the lower consumption in August. Slightly better correlations are obtained for Cotonou and Abidjan when the heat index (combining both temperature and relative humidity) are used to calculate the Cooling Degree-Days.



    Abbreviations: LAB: lactic acid bacteria; bac +: bacteriocinogenic; PGI: Protected Geographical Indication; S2BLAF: Lb. plantarum ST153Ch fresh; S2BLA: Lb. plantarum ST153Ch lyophilized; S2BC non-inoculated; S2BLAF + LM: Lb. plantarum ST153Ch fresh plus cocktail of L. monocytogenes; S2BLA + LM: Lb. plantarum ST153Ch lyophilized plus cocktail of L. monocytogenes; S2BC + LM: cocktail of L. monocytogenes

    "Alheira" is a traditional fermented smoked meat sausage, typical of the North of Portugal, which production dates back to the late fifteenth century. Nowadays, "Alheira" is a well-known Portuguese product with some varieties with Protected Geographical indication (PGI) status. "Alheiras" vary considerably in their final compositions and production processes. The relevant common elements in the production process are the boiling of various meats in lightly salted and spiced water; soaking the thinly sliced bread in some of the broth, formed during the boiling of the meats, until it is soft enough; adding meat in small pieces, spices and olive oil and/or fat drippings to the bread/broth mixture; there is no addition of starter cultures; stuffing the paste into pork intestinal or cellulose-based casings when everything is completely mixed and the salt and spices adjusted to the desirable taste (variable). Traditionally, "Alheiras" are smoked at low but uncontrolled temperature (generally < 37 ℃) and uncontrolled humidity [1], but industrially smoking is performed at higher temperatures, in ovens, and cooled rapidly in cooling chambers. Several authors observed that LAB constitute the predominant microbiota of "Alheira", with particular incidence of Lactobacillus spp. and Enterococcus spp. [1,2] from the early stages of fermentation process (during the smoking). However, pathogenic organisms, such as L. monocytogenes, Salmonella spp. and St. aureus have already been detected [2,3] which could affect the safe consumption of this food. "Alheira" have a pH ~5.1, 1.1% NaCl, ± 0.95 of water activity, 52.3% of moisture, 13% of lipids, 14% total protein and 13% of carbohydrates. These physicochemical parameters and composition, indicated that, and according to the accepted limits for these parameters, pH, salt content and moisture per se, do not assure the microbiological safety of this product. Furthermore, this is a product that is consumed cooked, but some studies suggested that internal temperatures may often not be sufficient to kill all of the pathogens originally present [4], which could justify the use of bioprotective cultures or other natural antimicrobial components.

    One of the main challenges in the production of traditional fermented meat products is to improve competitiveness by identifying innovations, which comply with safety policies and regulations, and guarantee food safety, while at the same time meeting consumer demands and expectations [5]. There is an increasing interest in the use of LAB as natural preservatives, due to the potential production of antimicrobial metabolites such as bacteriocins [6,7,8]. So, LAB cultures and/or their bacteriocins are very important for bioprotective strategies to control spoilage and pathogenic microorganisms such as L. monocytogenes [6,7,8,9]. It is recommended to use strains that are well adapted to specific environment characteristics of the food matrices in which they are going to be used, preferably isolates recovered from these or from similar foods, for optimal performance and bacteriocin production [9,10,11].

    Lactobacillus plantarum ST153Ch originally identified as Lb. sakei but subsequently re-identified as Lb. plantarum, is an autochthonous strain isolated from "Salpicão", a traditional Portuguese salami-like product [12]. Vaz-Velho et al. [13] showed the possibility of using this strain as a protective culture to improve the safety of fermented meat sausages with respect to L. monocytogenes. Todorov et al. [12] showed that this strain produces an anti-listerial bacteriocin (ST153Ch) that is heat resistant and stable at low temperatures, stable between pH 2.0 and 10.0 and produced at higher levels during the stationary phase of fermentation in the presence of 2% (w/v) D-Glucose [12].

    Food safety challenges are constantly changing and require new approaches and tools. Omics methodologies are rapidly transforming our approaches to the detection, prevention, and removal of foodborne pathogens [14], but also to monitor changes in microbiota composition of these products, the interactions between new bio-control agents and microbiota, which is directly correlated to its taste, aspect, safety and functional characteristics [15,16]. This could be a very interesting approach for industrial application.

    The aim of this work was to evaluate the effect of a fresh and a lyophilized bioprotective Lb. plantarum ST153Ch culture, at an industrial scale, on the microbiological characteristics of "Alheira de Vitela", during the 90 days of storage at 4 ℃. Simultaneously the antilisterial activity of Lb. plantarum ST153Ch was also evaluated. Changes in microbiota were also investigated by 16s rRNA gene analysis in "Alheira de Vitela" produced with and without the bacteriocinogenic culture.

    Lactobacillus plantarum ST153Ch (formerly Lc. sakei [12] and reclassified following 16 sRNA sequencing) was grown in de Man, Rogosa Sharpe (MRS) broth (Lab M, Bury, UK) at 30 ℃ for 48 h.

    Listeria monocytogenes Scott A [17] and L. monocytogenes 3701 (isolated from a fermented meat sausage (ESB-UCP) were grown in Tryptone Soy Broth (TSB; Biokar) supplemented with 0.6% (w/v) of yeast extract (LabM; TSBYE) at 37 ℃ for 18–22 h.

    All bacterial strains were sub-cultured twice under appropriate conditions before use in experiments. All strains were stored at −20 ℃ in the presence of 30% (v/v) glycerol.

    One liter of MRS broth was inoculated (1% v/v) with Lb. plantarum ST153Ch and after 24 h at 30 ℃, cells were centrifuged at 17200x g, for 10 minutes, at 4 ℃. Cells were resuspended in 100 mL of sterilized deionized water, in order to have 109 CFU/mL. For fresh culture, cells were kept at 4 ℃ until the inoculation procedure (fresh culture was prepared on the same day of inoculation).

    For preparation of lyophilized culture, the method used was adapted from Barbosa et al. [18]: cells were initially frozen at −80 ℃ overnight, and then desiccated under vacuum (2 ATM) for 4 days in a freeze-drier (SB4 Armfield, UK) at room temperature; and the condenser was cooled at −48 ℃. Before lyophilization process, Lb. plantarum ST153Ch reached ~1010 CFU/mL and after the concentration was ~109 CFU/mL.

    "Alheira" samples were manufactured in a meat plant according to traditional recipes and techniques. The ingredients used were as follows: Veal meat (35%), wheat bread (wheat flour, baking powder and salt), pork meat, rooster, cooking broth, spices, onion, olive oil and salt. The sausage mixture, before stuffing in natural casing, was divided into three similar portions, each one with 45 kg: one was inoculated with Lb. plantarum ST153Ch fresh (S2BLAF), another with Lb. plantarum ST153Ch lyophilized (S2BLA) and the other one was non-inoculated (S2BC), to act as a control. Lactobacillus plantarum ST153Ch, fresh or lyophilized (prepared as indicated in section 2.2) was added before stuffing, in order to reach ~108 CFU/g in the final product. Lyophilized culture was suspended in 3 L of water and sucrose (6%w/v) and then added to the fresh sausage mixture. Fresh culture was directly added to the sausage mixture. Sausages were smoked in ovens, with a slight smoke of holm-wood, in controlled humidity, for some hours. Then, "Alheiras" were packed (in packages composed with materials of polyolefin and polyamide), under modified atmosphere (20% CO2 and 80% N2) and stored for 90 days at 4 ℃. Three independent batches were produced.

    Microbiological analyses of all the samples were performed before smoking (−1 day), immediately after smoking (0 day) and during storage at times 3, 7, 15, 21, 28, 60 and 90 days.

    Listeria monocytogenes strains were sub-cultured twice (24 h at 37℃) in TSB broth using a 1% v/v inoculum. Each culture was centrifuged and mixed in Ringer's solution. An aliquot (300 µL) of the cocktail of L. monocytogenes suspension (107 CFU/mL for each strain of L. monocytogenes) was inoculated with a sterilized syringe in 300 g of each sample of "Alheira". This procedure was done after smoking, in the microbiology laboratory, in order to reach 105 CFU/g of sample of "Alheira": one batch was inoculated with Lb. plantarum ST153Ch fresh plus cocktail of L. monocytogenes (S2BLAF + LM), another batch with Lb. plantarum ST153Ch lyophilized plus cocktail of L. monocytogenes (S2BLA + LM) and the other one only with cocktail of L. monocytogenes (S2BC + LM), to act as a control.

    Twenty-five gram samples were added to 225 mL of sterile buffered peptone water (Biokar Diagnostics, Beauvais, France), and homogenized in a stomacher for 2 min. Appropriate decimal dilutions were prepared in Ringer's solution for microbial enumeration according to ISO Standards: LAB on de Man, Rogosa and Sharpe Agar (MRS, Biokar Diagnostics; [19]) incubated at 30 ℃ for 72 h; Enterobacteriaceae on RAPID' Enterobacteriaceae medium (Bio-Rad, CA, USA; [20]) and St. aureus on Baird Parker Agar (BPA, Bio-Rad; [21]), with rabbit plasma fibrinogen confirmation [22], both incubated at 37 ℃ for 48 h; Escherichia coli on Tryptone Bile X-glucuronide Agar [23] incubated at 44 ℃ for 24 h; Bacillus cereus on Mannitol Egg York Polymyxin Agar Base (VWR International, Pennsylvania, USA; ISO 7932:2004/Amd 1 [24]), and yeasts and molds on Rose-Bengal Chloramphenicol Agar (Oxoid, Hampshire, UK; NP 3277-1:1987 [25]) incubated at 25 ℃ for 5 days. Also the detection of some agents was performed using the ISO methodologies: detection and enumeration of L. monocytogenes [26,27], detection of Salmonella spp. [28], sulfite- reducing Clostridium spores [29] and detection of Yersinia [30]. Every analysis was performed in duplicate.

    Ten grams of "Alheira" samples were homogenized individually in a stomacher for 2 min. From this, 1 g was used for DNA extraction. The DNA was extracted according to the instructions of "The GRS Genomic DNA Kit–Tissue–for Stool" (GRISP, Porto, Portugal).

    The samples used were: "Alheira" inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (S2BLA (−1 day)), after smoking (S2BLA (0 day)) and after 60 days of storage (S2BLA (60 days)); and the control non-inoculated (S2BC): before smoking (S2BC (−1 day)), after smoking (S2BC (0 day)) and after 2 months storage (S2BC (60 days)).

    Samples were prepared for Illumina Sequencing by 16S rRNA gene amplification of the bacterial community. The DNA was amplified for the hypervariable V3-V4 region with specific primers and further reamplified in a limited-cycle PCR reaction to add sequencing adapters and dual indexes. First PCR reactions were performed for each sample using KAPA HiFi HotStart PCR Kit according to the manufacturer's suggestions, 0.3 μM of each PCR primer: forward primer Bakt_341F 5'-CCTACGGGNGGCWGCAG-3' and reverse primer Bakt_805R 5'-GACTACHVGGGTATCTAATCC-3' [31,32] and 12.5 ng of template DNA in a total volume of 25 μL. The PCR conditions involved a 3 min denaturation at 95 ℃, followed by 25 cycles of 98 ℃ for 20 s, 55 ℃ for 30 s and 72 ℃ for 30 s and a final extension at 72 ℃ for 5 min. Second PCR reactions added indexes and sequencing adapters to both ends of the amplified target region according to manufacturer's recommendations [33]. Negative PCR controls were included for all amplification procedures. PCR products were then one-step purified and normalized using SequalPrep 96-well plate kit (ThermoFisher Scientific, Waltham, USA) [34], pooled and pair-end sequenced in the Illumina MiSeq® sequencer with the V3 chemistry, according to the manufacturer's instructions [33] at Genoinseq (Cantanhede, Portugal).

    All 16S rRNA data were analyzed with Kraken v1 [35] using the pre-built MiniKraken 8Gb database (https://ccb.jhu.edu/software/kraken/) with default parameters. At each taxonomic rank, classification falling below a threshold of 0.1% of all data were excluded from the subsequent analysis (i.e. classification represented by less than ~100 reads).

    The comparison of the growth of L. monocytogenes without and with the bacteriocinogenic culture (fresh or lyophilized) through the storage time was carried out by one-way Analysis of Variance (ANOVA). The LSD-Tukey test was used to determine the significant differences (p < 0.05) among group means. Statistical analysis was done using SPSS 24.0.0.0 software for Windows.

    Since food safety has become an increasingly important international concern, the application of antimicrobial peptides from LAB, that target foodborne pathogens without toxic or other adverse effects, has received great attention. LAB that produce bacteriocins with antilisterial activity have been used in fermented meat sausages as bioprotective cultures since the beginning of the 90's [6,7,36,37,38,39,40,41]. Our work follows this trendline, and expects to validate the effect of this bioprotective culture at industrial scale and over long storage times.

    The behaviour of L. monocytogenes and LAB in "Alheira" samples (control and inoculated with bacteriocinogenic culture) is presented in Figure 1 and Table 1, respectively. No differences were observed when L. monocytogenes strains were tested alone or combined in a cocktail (data not shown), showing that they don't interfere between them. In the first three days of storage, it was observed a slight increase in L. monocytogenes counts. In parallel, a more pronounced growth was registered for LAB achieving ca. 9–10 log CFU/g. Also, and as observed in Table 1, the decrease in pH occurs at the same time as the number of lactic acid bacteria increases. After this period and until day 15, counts of the pathogen remained at the same level in control samples and decreased ca. 0.5 and 1.0 log CFU/g in samples inoculated with lyophilized and fresh bacteriocinogenic cultures, respectively, while counts of LAB increased ca. 1 log cycle in all the samples. From day 15 until the end of storage, counts of LAB remained stable (varied less than < 1 log cycle) while counts of L. monocytogenes decreased in all the samples. Although there were no significant differences (p > 0.05) in the reduction of the pathogen between samples with and without bacteriocinogenic culture, there was a clear trend for a higher reduction of L. monocytogenes in the presence of the protective culture. It is important to highlight that results represent the mean of three independent batches produced at industrial level and consequently several factors, e.g. the composition of the indigenous microbiota of the raw materials and final products, were neither constant nor controlled. Most of the results published in previous articles were performed at laboratory or pilot scale [7,36,37,38,39,40,41]. The work published by Vaz Velho et al. [13] was developed at smaller industrial scale, with only fresh culture and only at 7 days of storage. Also, these authors showed a reduction of L. monocytogenes less than 1 log. This current work was performed at industrial scale, with all the variability resulting therefrom. Moreover, this work used two different forms of the starter culture: lyophilized and fresh and analyses were performed during 90 days of storage. This well demonstrates the importance of validating studies at the industrial level and the importance of following the entire processes, until the end of storage time. For the industry it is crucial to follow product alterations until the end of storage, especially if we keep in mind that consumers are increasingly looking for safe products with long shelf-life.

    Figure 1.  Behaviour of L. monocytogenes in "Alheira de Vitela", during storage at 4 ℃ in samples inoculated with Lb. plantarum ST153Ch and cocktail of L. monocytogenes. (■) S2BC + LM; () S2BLAF + LM; (□) S2BLA + LM. For each time, columns with the same letter are not significantly different (p > 0.05).
    Table 1.  Growth of lactic acid bacteria and pH values in "Alheira de Vitela", during storage at 4 ℃ in samples inoculated with Lb. plantarum ST153Ch and samples inoculated with Lb. plantarum ST153Ch plus cocktail of L. monocytogenes.
    "Alheira" with Lb. plantarum "Alheira" with Lb. plantarum plus cocktail of L. monocytogenes
    Samples S2BC (non-inoculated) S2BLAF (fresh) S2BLA (lyophilized) S2BC + LM (only inoculated with L. monocytogenes) S2BLAF + LM (Fresh) S2BLA + LM (lyophilized)
    Time (days) Log (CFU/g) pH Log (CFU/g) pH Log (CFU/g) pH Log (CFU/g) pH Log (CFU/g) pH Log (CFU/g) pH
    −1* 4.0 ± 0.12 5.45 ± 0.04 8.3 ± 0.55 5.65 ± 0.03 7.8 ± 1.5 5.56 ± 0.06 nd nd nd
    0 7.6 ± 1.10 4.72 ± 0.06 9.1 ± 1.46 4.75 ± 0.05 7.8 ± 1.9 4.65 ± 0.06 7.3 ± 0.95 4.70 ± 0.02 8.0 ± 1.66 4.73 ± 0.03 8.53 ± 1.48 4.80 ± 0.04
    3 9.0 ± 0.06 4.62 ± 0.03 9.3 ± 0.59 4.59 ± 0.03 9.8 ± 0.6 4.60 ± 0.02 9.3 ± 0.81 4.62 ± 0.01 9.8 ± 0.40 4.65 ± 0, 03 9.87 ± 0.06 4, 65 ± 0, 04
    7 9.7 ± 0.55 4.54 ± 0.09 9.5 ± 0.96 4.53 ± 0.04 9.2 ± 0.7 4.5 ± 0.07 10.3 ± 0.40 4.55 ± 0.05 9.4 ± 1.30 4.64 ± 0.05 10.07 ± 0.71 4.60 ± 0.03
    15 10.0 ± 0.41 4.39 ± 0.05 10.2 ± 0.46 4.42 ± 0.07 10.5 ± 0.5 4.40 ± 0.03 10.4 ± 0.31 4.44 ± 0.08 10.7 ± 0.42 4.57 ± 0.02 10.53 ± 0.55 4.50 ± 0.07
    21 11.0 ± 0.00 4.25 ± 0.06 10.6 ± 0.29 4.30 ± 0.08 11.1 ± 0.0 4.31 ± 0.10 10.8 ± 0.12 4.43 ± 0.06 10.4 ± 0.46 4.45 ± 0.06 11.07 ± 0.06 4.50 ± 0.04
    28 10.7 ± 0.67 4.11 ± 0.08 10.7 ± 0.69 4.14 ± 0.04 10.9 ± 0.2 4.14 ± 0.06 10.6 ± 0.87 4.13 ± 0.02 11.0 ± 0.20 4.25 ± 0.04 11.03 ± 0.06 4.20 ± 0.06
    60 10.9 ± 0.06 3.60 ± 0.04 10.8 ± 0.85 3.67 ± 0.02 10.6 ± 0.6 3.70 ± 0.08 10.7 ± 0.29 3.70 ± 0.02 10.7 ± 0.44 3.90 ± 0.07 10.97 ± 0.12 4.00 ± 0.07
    90 10.5 ± 0.15 3.58 ± 0.09 10.4 ± 0.44 3.52 ± 0.06 10.4 ± 0.4 3.54 ± 0.04 10.4 ± 0.17 3.57 ± 0.04 10.4 ± 0.40 3.65 ± 0.05 10.60 ± 0.44 3.70 ± 0.05
    * before smoking.

     | Show Table
    DownLoad: CSV

    The pathogenic organisms investigated, (Salmonella, S. aureus, Yersinia, sulphite reducing Clostridium spores and E. coli) were not detected in any sample. Also, counts of Enterobacteriaceae and B. cereus were below the detection limit of the enumeration technique for 0, 3, 7, 15, 21, 28, 60 and 90 days of storage (Table 2).

    Table 2.  Microbial characterization of "Alheira de Vitela" at 4 ℃ for 0, 3, 7, 15, 21, 28, 60 and 90 days of storage.
    Enumeration (log CFU/g) Presence in 25g
    "Alheira" Bacillus cereus Yeasts Moulds Enterobacteriaceae E.coli Coagulase positive staphylococci Listeria spp. Listeria monocytogenes Salmonella spp. SRC sporesa
    S2BC < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BC + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BC < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BC + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BC < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLAF + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    S2BLA + LM < 1.0 < 1.0 < 1.0 < 1.0 < 1.0 < 1.0
    Legend: aSRC spores; sulphite reducing clostridial spores, presence (+) or absence (−) in 1, 0.1 or 0.01 g sample.

     | Show Table
    DownLoad: CSV

    Overall, for 16S rRNA metagenomic analysis almost all reads generated (~99%) were classified into a taxonomic rank for all the "Alheira de Vitela" samples, for the different stages of fermentation and with and without the lyophilized bacteriocinogenic culture, with more than 95% of them being classified at the family and genus level (Table 3). Regarding family analysis, Leuconostocaceae and Lactobacillaceae predominated in all the samples (Figure 2A). Lactobacillaceae was the predominant family in samples with addition of bacteriocinogenic culture and Leuconostocaceae was prevalent in control samples. At the genus level, the microbiome distribution was similar to the family classification, with the main genus being Lactobacillus, Weissella and Leuconostoc (Figure 2B). At both taxonomic levels, higher microbial diversity could be observed before and after smoking, as compared to 60 days after storage where samples presented two main families or genus. Of note, "Alheiras" sampled after 60 days of storage (60 days) with the addition of bacteriocinogenic culture were mostly populated by Lactobacillus, whereas without this addition, "Alheiras" were equally populated by Leuconostoc and a higher portion of Weisella.

    Table 3.  Global results of the 16SrRNA metagenomic analysis using Kraken.
    Sample Total # of paired reads generated Percentage of classified reads (%)
    Total Up to family Up to genus Up to species
    S2BLA (−1 day) 88285 99.93 94.94 (0.15) 94.71 (0.35) 30.03 (1.08)
    S2BLA (0 day) 77182 98.71 98.59 (0.67) 98.02 (0.86) 50.32 (1.71)
    S2BLA (60 days) 83671 99.77 97.53 (1.07) 96.87 (1.19) 81.41 (1.74)
    S2BC (−1 day) 91240 99.91 98.17 (0.23) 98.07 (0.21) 55.04 (0.8)
    S2BC (0 day) 62382 99.08 98.72 (0.69) 98.52 (0.72) 84.29 (1.18)
    S2BC (60 days) 76074 99.64 97.39 (0.14) 96.98 (0.34) 68.14 (0.73)
    Percentages are relative to the total number of classified reads. Values in parenthesis refer to the percentage of reads excluded at each taxonomic rank. "Alheira de Vitela" non-inoculated as a control (S2BC) and inoculated with Lb. plantarum ST153Ch lyophilized (S2BLA): before smoking (−1 day), after smoking (0 day) and 60 days of storage (60 days).

     | Show Table
    DownLoad: CSV
    Figure 2.  Results for 16s rRNA gene analysis of microbial communities of "Alheira de Vitela". Reads were classified using Kraken and (A) families, (B) genus and (C) species identified are presented. Percentages are relative to the total number of reads classified at each taxonomic level. Classifications falling below 1% in all samples are not presented and are detailed in Supplementary Table S1.

    At the species level, it was possible to observe lower microbial diversity in the samples before smoking (labeled as −1 day), with the sample without culture presenting higher diversity than the sample with culture (Figure 2C). After smoking, the microbial diversity of the inoculated sample increased, especially when compared to the control, with a more pronounced trend after 60 days of storage, when microbial diversity was greater. Through time, in both tested conditions, a decrease in Leuconostoc and Weisella species could be observed, although this was more accentuated in samples with bacteriocinogenic culture, as only traces of Weisella species were observed and Leuconostoc species were only present below 7%. After 60 days of storage, the most predominant specie identified was Lb. sakei that, as mentioned above, was the previous classification of inoculated Lb. plantarum strain, which is likely due to the characteristics of the database used. Also, it is interesting to observe the change in microbiome of samples before and after fermentation, showing the role of LAB in this process. Although there are differences in the microbiota, these are slight, which makes it impossible to draw final conclusions. So, we believed that further studies are still needed to explain the observed differences.

    Studying the changes in microbial populations can provide useful information to follow natural fermentation dynamics, particularly when using bioprotective cultures. Many different researches have been carried out unravelling the structure of the microbial consortia in dairy [42,43], meat [44] and vegetable foods [45].

    Lactobacillus plantarum ST153Ch showed a slight antilisterial activity in situ, with a reduction of ~0.5–1 log until 15 days of storage of "Alheira de Vitela". However, until the end of storage, counts of L. monocytogenes decreased in all the samples, while counts of LAB remained stable. Additionally, no significant differences were found between application methodologies (fresh or lyophilized LABs), therefore industry might be able to choose the most appropriate technique according to their manufacturing process. From the analysis of the microbial communities it was confirmed the absence of the pathogens investigated by culture dependent methods and it was demonstrated that the addition of the bioprotective culture altered the microbiota of "Alheira". At different stages of fermentation with and without the bac + culture, Leuconostocaceae and Lactobacillaceae predominated in all samples and, as expected, Lactobacillus was the genus more prevalent in "Alheiras" sampled after 60 days of storage with the addition of the bacteriocinogenic culture.

    Nevertheless, the few differences observed, there was a reduction in the L. monocytogenes counts in the early storage days, which could justify the use of these cultures. For the industry it is very important to control the development of a pathogen at the beginning of storage time when products are already available to consumers.

    All authors declare no conflicts of interest in this paper.

    This work was supported by funding from the National Funds from the Fundação para a Ciência e a Tecnologia (FCT) through project UID/Multi/50016/2013, by the project "Biological tools for adding and defending value in key agro-food chains (bio-n2-value)", nº NORTE-01-0145-FEDER-000030, funded by Fundo Europeu de Desenvolvimento Regional (FEDER), under Programa Operacional Regional do Norte-Norte2020" and a post-doctoral fellowship SFRH/BPD/113303/2015 (author J. Barbosa), and by DEM@BIOFUMADOS-Demonstrador do Biofumados Tradição vs Qualidade-Produção de Enchidos e Fumados Tradicionais Portugueses (NORTE-01-0247- FEDER-017634), co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), under Programa Operacional Regional do Norte (PO Norte).

    We would like to thank Dr. João Paulo Gomes and Dr. Vitor Borges from the Bioinformatics Unit at Department of Infectious Diseases of the Portuguese National Institute of Health for their support in the bioinformatics analysis of this study.



    [1] IRENA (2012) Prospects for the African Power Sector; 60.
    [2] Pineau PO (2008) Electricity sector integration in West Africa. Energy Policy 36: 210-223. doi: 10.1016/j.enpol.2007.09.002
    [3] REN21 (2014) ECOWAS Renewable Energy and Energy Efficiency Status Report; 86.
    [4] Karekezi S, Kithyoma W (2002) Renewable energy strategies for rural Africa: is a PV-led renewable energy strategy the right approach for providing modern energy to the rural poor of sub-Saharan Africa? Energy Policy 30: 1071-1086.
    [5] ECREEE (2015) Politique d'Efficacité Énergétique de la CEDEAO; 74.
    [6] World Bank Group (2021) Access to electricity (% of population). Available from: https://data.worldbank.org.
    [7] Rojey A (2011) Comment assurer la transition énergétique; 37.
    [8] REN21 (2016) Renewables 2016 Global Status Report; 272.
    [9] REN21 (2015) Renewables 2015 Global Status Report; 251.
    [10] Schipper L, Meyers S, Howarth RB, et al. (1992) Energy efficiency and human activity: Past trends, future prospects. Cambridge; New York, NY, USA: Cambridge University Press; 400.
    [11] Akbari H (2005) Energy saving potentials and air quality benefits of urban heat island mitigation[Internet]. Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, 19.
    [12] Shahmohamadi P, Che-Ani AI, Ramly A, et al. (2010) Reducing urban heat island effects: A systematic review to achieve energy consumption balance. Int J Phys Sci, 11.
    [13] Hamlet AF, Lee S-Y, Mickelson KEB, et al. (2010) Effects of projected climate change on energy supply and demand in the Pacific Northwest and Washington State. Clim Change 102: 103-128. doi: 10.1007/s10584-010-9857-y
    [14] Aivalioti S (2015) Electricity Sector Adaptation to Heat Waves, 51.
    [15] Hernández L, Baladrón C, Aguiar JM, et al. (2012) A study of the relationship between weather variables and electric power demand inside a smart grid/smart world framework. 12: 11571-11591.
    [16] Akinlo AE (2009) Electricity consumption and economic growth in Nigeria: Evidence from cointegration and co-feature analysis. J Policy Model 31: 681-693. doi: 10.1016/j.jpolmod.2009.03.004
    [17] Bessec M, Fouquau J (2008) The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach. Energy Econ 30: 2705-2721. doi: 10.1016/j.eneco.2008.02.003
    [18] Isaac M, van Vuuren DP (2009) Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 37: 507-21. doi: 10.1016/j.enpol.2008.09.051
    [19] Franç ois B, Borga M, Creutin JD, et al. (2016) Complementarity between solar and hydro power: Sensitivity study to climate characteristics in Northern-Italy. Renewable Energy 86: 543-553. doi: 10.1016/j.renene.2015.08.044
    [20] Apadula F, Bassini A, Elli A, et al. (2012) Relationships between meteorological variables and monthly electricity demand. Appl Energy 98: 346-356. doi: 10.1016/j.apenergy.2012.03.053
    [21] Marvuglia A, Messineo A (2012) Using recurrent artificial neural networks to forecast household electricity consumption. Energy Procedia 14: 45-55. doi: 10.1016/j.egypro.2011.12.895
    [22] Scapin S, Apadula F, Brunetti M, et al. (2016) High-resolution temperature fields to evaluate the response of Italian electricity demand to meteorological variables: an example of climate service for the energy sector. Theor Appl Climatol 125: 729-742. doi: 10.1007/s00704-015-1536-5
    [23] Adjamagbo C, Ngae P, Vianou A, et al. (2011) Modeling the demand for electrical energy in Togo. Rev Energy Renouvelables 14: 67-83.
    [24] Beccali M, Cellura M, Lo Brano V, et al. (2008) Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area. Renew Sustain Energy Rev 12: 2040-65. doi: 10.1016/j.rser.2007.04.010
    [25] US Department of Commerce N (2020) What is the heat index? NOAA's National Weather Service.
    [26] Bertrand REYSSET (2008) Impact of climate change on the energy sector in France, 5.
    [27] Adom PK, Bekoe W, Akoena SKK (2012) Modelling aggregate domestic electricity demand in Ghana: An autoregressive distributed lag bounds cointegration approach. Energy Policy 42: 530-537. doi: 10.1016/j.enpol.2011.12.019
    [28] Ouédraogo IM (2010) Electricity consumption and economic growth in Burkina Faso: A cointegration analysis. Energy Econ 32: 524-531. doi: 10.1016/j.eneco.2009.08.011
    [29] Inglesi-Lotz R, Blignaut JN (2011) South Africa's electricity consumption: A sectoral decomposition analysis. Appl Energy 88: 4779-4784. doi: 10.1016/j.apenergy.2011.06.018
    [30] UNECA (2016) Nineteenth (19th) session of the intergovernmental committee of experts (ice-19) of West Africa. Outcome document draft conclusions and recommendations, 8.
    [31] IEA, OECD (2014) Africa Energy Outlook : a focus on energy prospects in Sub-Saharan Africa. World Energy Outlook Special Report. Fance: International Energy Agency, 242.
    [32] IEA, OECD (2015) Energy and Climate Change, World Energy Outlook Special Report. International Energy Agency, 200.
    [33] CIA (2015) World Factbook. Electricity Production/Countries of the World.
    [34] UNOSD (2016) Seminar on Mainstreaming Energy for Sustainable Development Goals (SDGs), Targets and Indicators into Statistical Programmes in Select African Countries, 27-29 June 2016, Addis Ababa, Ethiopia.
    [35] IEA (2014) World energy outlook 2014 factsheet: Energy in sub-Saharan Africa today.
    [36] World Bank (2017) Where we work. World Bank.
    [37] Perspective monde (2021) Togo-PIB par habitant ($ US constant 2010)|Statistiques. Outil pédagogique des grandes tendances mondiales depuis 1945 École de politique appliquée Faculté des lettres et sciences humaines.
    [38] Atlas Overview (2010) Climate|West Africa, 157.
    [39] AccuWeather (2016) Global Current Weather|AccuWeather.
    [40] Cedar Lake Ventures, Inc (2016) The Typical Weather Anywhere on Earth-Weather Spark.
    [41] Commonwealth of Australia (2002) Appliance Electricity End-Use: Weather and Climate Sensitivity, 69.
    [42] Robinson PJ (2001) On the definition of a heat wave. J Appl Meteorol 40: 762-775. doi: 10.1175/1520-0450(2001)040<0762:OTDOAH>2.0.CO;2
    [43] Masterton JM, Richardson FA (1979) Humidex : a method of quantifying human discomfort due to excessive heat and humidity. Meteorological Service of Canada, 45.
    [44] Rome S, Oueslati B, Moron V, et al. (2016) Heat waves in Sahel : definition and main spatial-temporal characteristics (1973-2014). In: J.-M. Fallot DJ & NB, éditeur. 29ème Colloque de l'Association Internationale de Climatologie, 345-350.
    [45] Oueslati B, Sambou M-JG, Pohl B, et al. (2016) Sahelian heat waves: characterization, mechanisms, predictability. 29e Colloque de l'Association Internationale de Climatologie. Besanç on, France: Association Internationale de Climatologie, 327-332.
    [46] Martin Bromley (2016) Degree Days : Understanding Heating and Cooling Degree Days.
    [47] Jiang F, Li X, Wei B, et al. (2009) Observed trends of heating and cooling degree-days in Xinjiang Province, China. Theor Appl Climatol 97: 349-360. doi: 10.1007/s00704-008-0078-5
    [48] BizEE Software (2020) Degree Days—An Introduction.
    [49] Aceituno P (1979) Statistical formula to estimate heating or cooling degree-days. Agric Meteorol 20: 227-232. doi: 10.1016/0002-1571(79)90023-2
    [50] d'AMBROSIO ALFANO FR, Palella BI, Riccio G (2011) Thermal Environment Assessment Reliability Using Temperature —Humidity Indices. Ind Health 49: 95-106. doi: 10.2486/indhealth.MS1097
    [51] Adjamagbo C, Ngae P, Vianou A (2011) Modélisation de la demande en énergie électrique au Togo, 17.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4490) PDF downloads(263) Cited by(14)

Figures and Tables

Figures(8)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog