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Citrus fruits are the most exciting family of ingredients with which to cook. They satisfy almost every part of the palate - sweet, sour, bitter, and umami-enhancing, how many other foods are as versatile and transformative? From the smallest squeeze of lemon, to the zing of lime zest, citrus fruits are almost magical. No longer seen as exotic, they are truly international. Take the humble lime: cornerstone of the American key lime pie, fragrant in Thai curries, fresh in Mexican guacamole, used to cook raw fish in South American ceviche, pickled in India, and dried in the Middle East. Citrus offers 150 inspiring recipes that celebrate these wonderful fruits. Through fresh salads, soups, seafood, Asian and Mediterranean-influenced meat dishes, preserves and pickles, to the world of sweet pies, cakes, and cocktails, Catherine Phipps explores the myriad uses of oranges and lemons, and all things in between. Her recipes are straightforward, easy to follow, and work perfectly every time. Citrus is a vibrant, colourful source of delight and inspiration.

Abbas Zainab, A35 Abbott Jane, A130 Abd Al-Shakour E, A222 Abdelrahim Ahmed, A212 Abdelrahim M, A4, A196 Abdelrahim Mohamed, A3, A17, A45, A160 Abdul-Razakq Humayra, A207 Abulafi Muti, A28 Acharjee Animesh, A113 Acheson Austin G, A30, A204 Acott Nathan, A170 Acres Meghan, A7 Adams Alex, A7, A96, A97, A108 Adams Gemma, A158 Adderley Nicola, A20, A104 Adilieje Chinemelu, A194, A195, A225 Adlard Francess, A63 Adu-Tei Sebastian, A32, A247 Agarwal A, A223 Agarwal Mitali, A194, A195, A225 Aggarwal Prashant, A172 Agrawal Anurag, A32, A158 Ahmad Ahmir, A42, A53 Ahmad Omer, A17, A119 Ahmad Omer F, A51, A59 Ahmad Tariq, A6, A88, A103, A128 Ahmed Jabed, A204 Ahmed Jamal, A100 Ahmed Laeeque, A43 Ahmed Raheel, A184 Ahmed Saeed, A42 Ahmed Salman, A204 Ahmed Wafaa, A43 Ainley R, A10 Akbar Ayesha, A99 Aktar Rubina, A33 Al-Bakir Ibrahim, A101 Al-Hakim Bahij, A160 Al-Hassi Hafid O, A30, A204 Al-Hillawi Lulia, A55 Al-Mukhtar Ahmed, A22 Al-Najim Werd, A19, A170 Al-Rifaie Ammar, A44 Al-Rubaiy Laith, A150, A156 Al-Shakarchi Nader, A117 Al-Shamma Safa, A14 Al-Zanbagi Adnan, A43 Ala Aftab, A41 Alababra Edward, A227 Alame Riad, A36 Alawode Deborah, A203 Albers David, A3 Albu-soda Ahmed, A143 Aldhwayan Madhawi, A19, A170 Aldridge Jodi, A36 Aleem Muhammad Junaid, A109 Alexakis Christopher, A85 Alexander JL, A208 Alexandre Leo, A43 Alhamamy Noor, A109 Alhazmi Ghadeer, A43 Ali Tamir, A171 Alkandari A, A196 Alkandari Asma, A160 Alkhoury Jad, A246 Allen Rupert, A32 Allen Ruridh, A97 Allen Stephen, A84 Allsop Caroline, A36 Almilaji Orouba, A37, A190 Alzoubaidi Durayd, A57, A58 Amani Lisa, A49 Ambrose Tim, A96, A97 Ambrose Timothy, A108 Anand Manraj, A204 Anderson John, A54, A57, A58, A167 Anderson Simon, A94, A116, A117, A129 Anderson-Leary Lois, A178 Andrade Matheus de, A141 Andrade Matheus De, A141 Andreasson A, A208 Andreica Elena-Cristina, A86 Andrew Mark, A109 Andrews C, A10 Andrews Tim, A38 Andreyev Daniel, A131 Andreyev Jervoise, A45 Ang Yeng, A45 Ansari A, A129 Ansari Azhar, A240 Ansari Saleem, A189 Anstee Quentin, A14, A153 Anstee Quentin M, A142 Antanaviciute Agne, A195 Antanavicuite Agne, A24 Appleby Richard, A120 Appleby Victoria, A135 Arasaradnam Ramesh, A47, A116, A132, A133, A134, A192 Arasaradnam Ramesh P, A200 Aravinthan Aloysius, A227 Archbold Sharon, A221 Archer Thomas, A44, A45, A194, A195, A225 Arebi Naila, A93 Arkir Zehra, A117 Arms-Williams Bradley, A132 Armstrong Lawrence, A27 Armstrong Paul Richard, A131 Arndtz S, A4, A196 Arndtz Sophie, A3, A17, A45, A160 Arnott Ian, A5, A10, A97, A98, A106, A112, A123, A232 Arora Arvind, A227 Arulrajan Sithhipratha, A186 Ashcroft Samuel, A223 Ashrafian Hutan, A19, A76, A170, A249 Aslam Nasar, A190 Assadsangabi Arash, A119 Atkinson David, A32 Atkinson Emma, A38 Au Andrea, A190 Aulicino Anna, A195 Austin Andrew, A139 Auth Marcus Karl-Heinz, A91 Auth Marcus KH, A84 Avades T, A46 Avades Tony, A129 Averill George, A82 Avery Leah, A142 Awadelkarim Bidour, A22, A23, A63, A171 Aziz Anthony, A240 Aziz Imran, A32, A180, A185, A239 Aziz Qasim, A33, A218 Babar Nauman, A237 Backhouse Laura, A223, A237 Badrulhisham Fakhirah, A237 Baer Hannah Moana, A83 Baggus Elisabeth MR, A179, A181 Bagla Nipin, A73 Bahhadi-Hardo Maria, A236 Baig Daniyal, A196, A224 Bailey A, A155 Bailey Adam, A31 Bailey Alara, A230 Bailey James, A191 Bailey Paul, A80 Baillie Samantha, A194 Bain Gillian, A107, A112 Bain Iain, A181 Bain Kym Antionette, A83 Bains Vikram, A55 Baker Alexandra, A202 Baker Ann-Marie, A8 Baker Anne-Marie, A29 Baker James, A172 Baker Katherine, A9 Bakir Ibrahim Al, A9 Bakshi Nikul, A116 Baldwin Tania, A80, A243 Banbury Carl, A124, A125 Bandel-Kuchorew Timothy, A123 Banerjea Ayan, A191 Banerjee Deepanjali, A181 Banerjee Rajarshi, A153 Banerjee Rwiti, A120 Bangera Sachin, A73 Banim Paul JR, A173 Banks M, A4 Banks Matt, A163 Banks Matthew, A17, A165 Bannaga Ayman, A132, A133, A134 Bannur Uday, A227 Barclay Andrew R, A27 Barclay Stephen, A136, A157 Barker Rosemary, A82 Barki Natasja, A182 Barmpoutis Panagiotis, A20 Barnes Daniel, A205 Barnes Ellie, A174 Barnes Tom, A190 Barr A, A91 Barr Amy, A90 Barr Hugh, A163 Barragry John, A186 Barrett Diane, A27 Barrett K, A10 Barry Peter, A221 Bartucz Renata, A144 Basford Pete, A56 Bassett Paul, A166 Bassil S, A10 Basumani Pandurangan, A41 Batchelor Suzi, A83 Bath Philip, A219 Bazarova Alina, A2 Beales Ian, A99 Beaton David, A46, A47 Bedford H, A91 Bedford Hugh, A90 Bedossa Pierre, A14, A153 Beech Nicola, A241 Beggs Andrew D, A30, A113, A204 Beintaris I, A2 Beintaris Iosif, A45, A46, A54 Beirne Paul, A212 Bekkali Noor, A69 Belhaj Karim, A246 Bell G, A10 Belnour Salma, A84 Beltran Luisa, A117 Benjamin Nibbs Robert John, A83 Benjamin Walter, A3 Benton Sally, A28 Berditchevski F, A134 Berrill James, A228 Berrington Janet, A28 Berry Alexander, A132 Berry S, A10, A86 Besherdas Kalpesh, A55, A72, A164, A169, A203 Bethea Jane, A13 Bettey Marion, A130 Bevan R, A50 Bevan Roisin, A48 Bevis Edwin DA, A247 Bewick Gavin, A111 Bewshea Claire, A103 Bhala Neeraj, A128 Bhala Nij, A104 Bhalme Mahesh, A146 Bhandare Anirudh, A159 Bhandari P, A4, A196 Bhandari Pradeep, A2, A3, A17, A45, A56, A57, A58, A160 Bhardwaj-Gosling R, A50 Bhatnagar G, A10 Biagi Federico, A24 Bianchi Michele, A165 Bird-Lieberman Elizabeth, A31 Birdi Siddharth, A244 Bishop Sophia, A94 Bisschops Raf, A1, A2 Bisschops Raff, A17 Biswas Sujata, A31 Black Christopher, A35 Black Christopher J, A34, A208 Blackwell Clare, A5 Blackwell J, A10, A101 Blackwell Jonathan, A85 Blackwell S, A91 Blackwell Sue, A90, A116 Blad William, A40, A111, A223 Bloom S, A10 Bloom Stuart, A85, A110, A117, A185 Bloxham David, A25 Bodger Keith, A15, A85, A114, A241 Boer Nanne de, A129 Boger Phil, A48, A57, A58, A79 Boger Philip, A3, A73 Bolimowska OO, A134 Bolland Elizabeth, A139 Bolognini Daniele, A182 Bonnington Stewart, A21 Borca Florina, A188 Borg-Bartolo Simon, A119 Borrow Dean-Martin, A227 Bostina Roxana, A233 Bottle A, A101 Boursier Jerome, A14 Bouvier Catherine, A23 Bowes Helen, A220 Boylan Conor, A27 Boyle Marie, A142 Boyle Nicholas, A162 Bradberry Sally, A131 Brady JM, A155 Brain Oliver, A89, A96, A97, A108, A195 Bramwell C, A10 Brandao Patrick, A17 Braniff Conor, A147 Brennan Amanda, A221 Bridge S, A86 Brindle William, A97 Broad Sophie, A86 Brodbeck Jens, A102 Brookes M, A10 Brookes Matthew, A10, A100, A116, A235 Brookes Matthew J, A30, A204 Broughton Raphael, A47, A76, A248, A249 Brown Adrian, A82 Brown Jonathon, A107 Brown SR, A10 Brownlee Ann, A138 Brownlee Nicola, A231 Brownson Emily, A87 Bruggeman Nina, A25 Buchanan Elaine, A27 Buchanan Ryan, A41 Buchanan Vanessa, A98 Buck Amy, A7 Buckle Rachel L, A32, A185, A239 Buckley Martin, A99 Buddika Mahesh Pasyodun Koralage, A170 Buenaventura Anna, A242 Bungay Helen, A174 Burch N, A10 Burgess Natasha, A94 Burke Laura, A135 Burman A, A10 Burton Paul, A82 Butterworth Jeff, A4 Byrne Debra, A119 Byrne James, A19, A170 Caestecker John De, A42 Caestecker John de, A163 Cahill Aidan, A97 Cain O, A134 Cairnes Vida, A6 Cama Rigers, A72, A169, A203 Campbell B, A50 Campbell Fiona, A38 Campbell Iona, A112 Campbell Jennifer, A22 Campbell Matthew D, A143 Campbell MD, A150 Campbell Samantha, A49, A55 Cananea Elmie, A173 Canda Ralph, A164, A169 Cannatelli Rosanna, A2, A124, A125 Carberry Isabel, A194, A195, A225 Carbone Marco, A13, A153 Card Timothy, A87, A191 Cardigan Tracey, A27 Carey Christopher D, A7 Carlton Jill, A137 Caro Simona Di, A186 Carras Efrossini, A223 Cartmell Mark, A206 Casey John, A212 Cash Johnny, A147 Cassidy Sophie, A142 Catlow Jamie, A48, A49, A50 Ceney Adam, A66 Cesano Sara, A98 Chadebecq François, A51 Chadwick Georgina, A29, A51 Chai Angeline, A242 Chako Jerry, A197 Chakrabarty Gayatri, A135 Champsi Asgher, A109 Chanchlani Neil, A6, A103 Chandan Joht, A104 Chandrakumaran Kandiah, A23 Chandrapalan Subashini, A192 Chapman Caroline, A191, A193 Chapman Elinor, A172 Chapman Michael, A65 Chapman Sarah, A10 Chapman Thomas, A37, A88, A108 Chapple KS, A201 Character Consortium IBD, A7 Charles-Nurse Shonette, A70 Charnely Richard, A69 Charnley Richard, A21 Chaudhry Rayhan, A150 Chauhan Abhishek, A135 Cheah Ramsah, A212 Chee Desmond, A6, A88, A103 Cheent Kuldeep, A236 Chen Chien Chuan, A1 Chen Frederick, A135 Chen Hannah, A195 Chen Joshua, A175, A181 Chen Michelle, A28 Cheng Ivy, A210, A222 Cheng Jeng, A91 Cheung Vincent, A89, A195 Chew Thean S, A114 Chew Yun, A194, A195, A225 Chey Chia Sin, A135 Chhina Navpreet, A19, A170 Childs Wendy, A116 Chin Jun Liong, A226 China Louise, A41 Ching Hey-Long, A247 Chirnaksorn Supphamat, A212 Chitnis Meenali, A195 Chiu Monica, A176 Choi Chang-Ho Ryan, A9, A101 Chopra Tanya, A100 Choudhary Anisa, A33 Chouhan Manil, A165, A174 Christina E, A26 Christodoulou Katja, A176 Chuah Cher, A112 Chuah Cher Shiong, A12, A52 Chung Chen-Shuan, A1 Chung Yooyun, A82, A89 Chung-Faye Guy, A65, A70, A86, A89 Church Nicholas, A52 Churchill Timothy, A223 Cianci Nicole, A166, A223 Cinar Cigdem, A201 Clark Clare M, A6, A27 Clark Laura, A229 Clark Tanya, A119 Clarke Carolyn, A7 Clarke Christopher, A227 Clarke EJ, A186 Clarke Emily J, A188, A189 Clarkson Jill, A239 Clement Dominique, A173 Cleminson Jemma, A176 Cliff Rebecca, A244 Clifford J, A10 Clough Jennie, A90 Coates E, A91 Coates Elizabeth, A90 Cococcia Sara, A151, A152 Cole Alexander, A93 Cole Andy, A45 Cole John J, A83 Coleman Helen, A195 Coleman Mark, A74, A230, A249 Coleman P, A142 Coleman Sarah H, A179, A180, A181 Collantes Elena, A195 Colleypriest Ben, A177 Collier J, A155 Collins Carole, A51 Collins Daniel, A107 Collins Paul, A38, A91 Coltart Iona, A236 Colwill Michael, A86 Conley

The processing of Citrus grandis Osbeck cv. Mato Peiyu (CGMP) fruits generates a considerable amount of waste, mainly the flavedo, albedo, and segment membrane; the generated waste yields severe environmental and economic challenges. In this study, we tried to reclaim some functional chemicals from the waste. Our data indicated that the essential oil content in the flavedo was 0.76-1.34%, with the major component being monoterpenes (93.75% in August, declining to 85.56% in November, including mainly limonene (87.08% to 81.12%) and others such as β-myrcene). p-Synephrine (mg/100 g dry weight) declined accordingly (flavedo, 10.40 to 2.00; albedo, 1.80 to 0.25; segment membrane, 0.3 in August, 0.2 in September, and none since October). Polyphenols (in μg/g) included gallic acid (70.32-110.25, 99.27-252.89, and 105.78-187.36, respectively); protocatechuic acid (65.32-204.94, 26.35-72.35, and 214.98-302.65, respectively), p-coumaric acid (30.63-169.13, 4.32-17.00, and 6.68-34.32, respectively), ferulic acid (12.36-39.36, 1.21-10.25, and 17.07-39.63, respectively), and chlorogenic acid (59.19-199.36, 33.08-108.57, and 65.32-150.14, respectively). Flavonoids (in μg/g) included naringin (flavedo, 89.32-283.19), quercetin (181.05-248.51), nobiletin (259.75-563.7), hesperidin, and diosmin. The phytosterol content (mg/100 g) was 12.50-44.00 in the flavedo. The total dietary fiber in the segment membrane was 57 g/100 g. The antioxidant activity against the DPPH[sup.*] and ABTS[sup.+*] free radicals was moderately high. In conclusion, the waste of CGMP fruits is worth reclaiming for essential oil, p-synephrine, polyphenolics, and dietary fiber. Notably, p-synephrine content (flavedo: <8 mg/100 g dry weight, albedo: <2.0, or segment membrane: <0.4 mg) can serve as a marker of the internal maturation of CGMP fruits.

This poem was published in volume 18, number 4, of Monthly Review (September 1966), from Selected Poems of Pablo Neruda, trans. Ben Belitt (Grove Press, 1961).

In weekly trips to the farmers' market with his father, a boy learns about seasonal fruits and vegetables, and about patience.

There is a growing interest in Corema album (L.) D. Don fruits due to the unique white colour, mildly acidic lemony flavour and health-promoting properties associated with its bioactive composition. This study performs a physical-chemical characterisation of cultivated C. album fruits from a multi-origin clonal field. The field comprises ten wild populations with distinct geographical origins, grown under the same edaphoclimatic conditions. We analysed fruits CIELab colour parameters, texture profile (TPA), pH, acidity (TA, g.100 mL.sup.-1), soluble solids content (SSC, %) and total phenolic content (TPC, mg CAE.100 g.sup.-1). Our results showed differences between fruits physical-chemical attributes. Variation patterns in fruits SSC and hardness suggest that the differences might be related to the original geographical location of the populations. The determined TPC levels in all samples were very encouraging at a bioactive level, ranging from 185.3 to 355.6 mg CAE.100 g.sup.-1. Fruits from Mira and Pego populations stood out from the ten geographical provenances. Mira fruit samples had higher sweetness and lower acidity, while the Pego ones had firmer fruits and higher phenolic content. The multi-origin clonal field allowed us to offer an interesting scientific comparative background, highlighting the large potential of these berries for introduction in the commercial market. Not only our results support the potential of white crowberry as a new crop; the detected differences also indicate a hidden capacity for small fruit market diversification.

As young workers prefer urban labors and migrate to USA and Canada, mango harvesting is becoming scarce on Mexican coasts. This seasonal labor is becoming expensive and when many orchards produce fruit simultaneously, grower losses increase. In this research, an innovative fruit detachment method was tested after applying a viscous paste to the pedicel of mango fruits hanging in the tree. Activated carbon or charcoal (AC), was mixed with different amounts of nitric acid to provide three AC composite blends named: light, medium, and dense. The nanomaterial was applied with a brush to the fruit pedicel/peduncle taking up to 4 h before the mango fruits felt to a net below the tree canopy. Mango detachment experiments indicated that the medium blend was the most efficient in releasing the fruit, taking an average of 2 h. The dense nano-material decreased latex exudation to 7% of the fruits. Fruit maturity emerged as a crucial factor for detachment time, followed by mango weight.

To achieve the rapid recognition and accurate picking of Camellia oleifera fruits, a binocular vision system composed of two industrial cameras was used to collect images of Camellia oleifera fruits in natural environments. The YOLOv7 convolutional neural network model was used for iterative training, and the optimal weight model was selected to recognize the images and obtain the anchor frame region of the Camellia oleifera fruits. The local binary pattern (LBP) maps of the anchor frame region were extracted and matched by using the normalized correlation coefficient template matching algorithm to obtain the positions of the center point in the left and right images. The recognition experimental results showed that the accuracy rate, recall rate, mAP and F[sub.1] of the model were 97.3%, 97.6%, 97.7% and 97.4%. The recognition rate of the Camellia oleifera fruit with slight shading was 93.13%, and the recognition rate with severe shading was 75.21%. The recognition rate of the Camellia oleifera fruit was 90.64% under sunlight condition, and the recognition rate was 91.34% under shading condition. The orchard experiment results showed that, in the depth range of 400–600 mm, the maximum error value of the binocular stereo vision system in the depth direction was 4.279 mm, and the standard deviation was 1.142 mm. The detection and three-dimensional positioning accuracy of the binocular stereo vision system for Camellia oleifera fruits could basically meet the working requirements of the Camellia oleifera fruit-picking robot.